Systems and methods for revascularization assessment

ABSTRACT

Disclosed herein are systems and methods for revascularization assessment. The methods can in some cases include one or more of the steps of measuring blood perfusion as a function of time to obtain time series data, mathematically transforming the time series data into a power spectrum, calculating at least one parameter of the power spectrum within a specific frequency range, and using the at least one calculated parameter as a discriminator for the first population and the second population.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. §120 as acontinuation of U.S. patent application Ser. No. 14/460,231 filed onAug. 14, 2014, which in turn claims the benefit under 35 U.S.C. §119(e)as a nonprovisional application of U.S. Prov. App. No. 61/865,977 filedon Aug. 14, 2013, as well as U.S. Prov. App. No. 61/888,790 filed onOct. 9, 2013. This application is also related to U.S. application Ser.No. 13/967,298 filed on Aug. 14, 2013. Each of the foregoingapplications is hereby incorporated by reference in their entireties.

BACKGROUND Field

This disclosure relates to the measurement of blood flow in tissue, inparticular measurement of blood flow in the foot or other extremities.

Description of the Related Art

The rapidly aging population in the developed world has led to anincreasing prevalence of aging-associated degenerative diseases such asperipheral arterial disease and Type 2 diabetes. The manifestations ofthese include tissue ischemia, chronic wounds and diabetic foot ulcers,where lack of appropriate treatment may lead to infection, gangrene and,in the case of foot ischemia, partial or complete amputation of one orboth feet.

Peripheral arterial disease (PAD) is a progressive disease in whichnarrowed or obstructed arteries reduce blood flow to the limbs. PAD canresult from atherosclerosis, inflammatory processes leading to stenosis,an embolism, or thrombus formation, and is associated with smoking,diabetes, dyslipidemia, and hypertension. PAD can if untreated result incritical limb ischemia (CLI), in which blood flow to the limb (usuallythe legs and feet) is compromised to such an extent that tissue damageensues with consequent ulceration, gangrene or loss of the limb.Patients with PAD are also at a disproportionately high risk of othercardiovascular diseases like myocardial infarction and stroke and ofdeath as a result of these conditions. With the incidence of diabetesincreasing worldwide, treatment of CLI and prevention of disability andof limb loss from it has become a significant health priority.

Peripheral vascular intervention procedures using endovascular(minimally invasive) intervention, open surgery or a combination of thetwo are currently the only methods available to restore perfusion to thelimbs in patients with PAD. Medical management can help only to delaythe progression of the disease, if at all. However, clinicians currentlylack the intraoperative tools to properly assess perfusion in theaffected tissue, usually in the feet, in real-time to reliably guide theconduct of the interventional procedure. Existing technologies thatmeasure blood perfusion include skin perfusion pressure (SPP), duplexultrasound (DUX), and transcutaneous oxygen monitoring (TCOM). Each ofthese techniques suffers from one or more disadvantages. SPP onlyprovides perfusion data at the skin dermis level, requires the skintemperature to be normalized to 44° C., is affected by skin pigmentationand is unreliable with patients with edema. SPP also requires the use ofa pressure cuff, which further limits its utility as a real-timeperfusion assessment tool during peripheral vascular interventions. DUXdoes not assess tissue perfusion but instead measures blood flow inlarge vessels (>1.5 mm). TCOM requires the patient to be placed onhyperbaric oxygen, making it incompatible with the cath lab/operatingroom. Furthermore, TCOM does not provide real time revascularizationdata as it takes about 4 to 6 weeks for the measurements to equilibrate.

Accordingly, there is a need for noninvasive, real-time measurement ofblood perfusion in a range of blood vessel sizes and in the tissuesupplied by these vessels. In particular, there is a need fornoninvasive, real-time measurement of blood perfusion in the foot thatcan be reliably performed as the interventional procedure proceeds andbe used to inform the decision making during the procedure.

Ischemia is a condition where a restriction of blood supply to tissuesleads to a shortage in oxygen and glucose, resulting in irreversibledamage to tissues. If discovered too late, reperfusion of blood byvarious treatment options, thrombolytic or surgical, will only furtherincrease the damage to the tissue as opposed to rescuing the tissue. Forexample, one of the most common sites of ischemia is the foot. In thiscase, early detection and diagnosis of an ischemic foot at risk isimperative, before the damage becomes irreversible. Currently, the mostcommon way to diagnose an ischemic foot is ABI (Ankle Brachial Index)which compares the blood pressure in the arm with that at the ankle. AnABI measurement less than 0.9, in some cases, is indicative of anischemic foot. However, ABI measurements are highly dependent onoperator protocol, i.e. different values can be obtained whenmeasurements are obtained with the subject in a seated or supineposition, or when the operator uses a different measurementprotocol/equipment. ABI also produces falsely elevated measurements incalcified vessels of patients who have diabetes mellitus, are receivinghemodialysis, or if there is an extensive distal arterial lesion belowthe ankle (Yamada et al, J Vasc Surg 2008; 47: 318-23).

A chronic wound is a non-healing wound that shows little or noimprovement after four weeks or does not heal in eight weeks. Inpractice, patients may present with chronic wounds that remain open forover a year. Around the world, there are 37 million people who sufferfrom chronic wounds, mostly on the lower limbs. In the US alone, chronicwounds have affected 6.5 million patients and accounted for $1.4 billionin spending in 2010. Since chronic wounds are associated with thediseases of aging, such as diabetes and obesity, the healthcare need forchronic wound management is rising together with the rise in agedpopulations in the developed world. The early diagnosis of a chronicischemic wound on lower limbs is particularly important, as it has amajor impact in determining whether conservative wound management (e.g.,bandages and moist dressings) would be sufficient, or whether moreaggressive therapies are required to forestall further wounddeterioration that may culminate in amputation.

Conservative therapy for wounds (e.g., bandages, moist dressings) cansuffice to facilitate wound healing if the blood perfusion around thewound tissue is not compromised beyond the minimal threshold for passivehealing to occur. In cases where the perfusion is compromised, however,the inappropriate use of conservative wound therapy causes a time lagbetween the first presentment of a wound in a clinical setting to aneffective therapy commensurate with the seriousness of the woundcondition.

The single most important determinant of tissue viability in a wound isits blood supply. The ability to assess the blood perfusion around thewound bed allows clinical decisions to be made regarding either (a)continuation of conservative therapy if tissue is viable or, (b) ifblood perfusion is too severely compromised for successful conservativetherapy, to progress early to more advanced wound care products likechemical debriding agents, or advanced wound therapies such as topicalnegative pressure, hyperbaric oxygen therapy (“HBOT”), etc. Inappropriate cases, the patient can be directed to revascularization byperipheral interventional procedures. Hence, a blood perfusion monitorthat can facilitate the early streaming of patients into conservativeversus aggressive wound therapies is highly desirable.

HBOT involves the administering of oxygen at levels 2-2.5 times sealevel in a hyperbaric chamber. A patient may be prescribed up to 40sessions of HBOT, with typically 3-4 sessions per week, in order tomaximize the delivery of oxygen to chronic wound tissue. Such therapy isexpensive and is not without risk; its side effects include ear andsinus barotrauma, paranasal sinuses and oxygen toxicity of the centralnervous system. (Aviat Space Environ Med. 2000; 71(2):119-24.) Moreover,a retrospective study of 1144 patients (Wound Rep Reg 2002; 10:198-207)indicated that 24.4% of chronic wound patients received no benefit fromit. Therefore, a diagnostic device to better predict the success of HBOTin chronic wound treatment will help to avoid unnecessary and unhelpfultherapy, and obtain significant cost savings in the healthcare system.

In foot ischemia cases where amputation is required, there is a need fora new diagnostic tool that can better guide decisions regarding theamputation level, by predicting the potential success of amputationwound healing. Amputation is typically performed on patients with severelimb ischemia who cannot be treated with reconstructive vascularsurgery, patients with diabetic foot ulcers or venous ulcerations.Approximately 85-90% of lower limb amputations in the developed worldare caused by peripheral vascular disease and poor wound healingaccounts for 70% of the complication cases that arise from amputation.Due to the lack of optimal tools to predict amputation healing,physicians have to make subjective judgments on the best site foramputation, and since the bias is to maximize limb preservation, it isnot uncommon for a patient to require a subsequent amputation higher upthe leg when the first amputation wound is unable to heal. The healingrate of below-knee amputation ranges between 30 and 92%, with are-amputation rate of up to 30%. Thus, an accurate tool for predictingsuccessful amputation healing is needed to help doctors more accuratelydetermine the site of amputation that will result in maximal limbpreservation while avoiding the trauma and cost of a revisionamputation.

Generally in surgical procedures, particularly in plastic andreconstructive surgery, tissue flaps are used to cover wound defects.These may be either pedicled flaps (i.e. have a vascular pedicle oftheir own that supplies blood to the flap) or free-flaps that needmicrovascular connections with the recipient site to ensure adequateblood supply. Both types of flaps are crucially dependent on the bloodperfusion within them for the flaps to survive. Flap perfusion needsclose monitoring especially in the first few hours to days after thereconstruction procedure and early detection of loss of perfusion willhelp to direct the patient for further surgical procedures as needed toensure continued flap viability. It will thus be useful if a diagnostictool can potentially be used to monitor flap blood perfusioncontinuously in the post-operative period and prevent flap loss due todelayed detection of flap ischemia.

Currently, diagnostic devices on the market for wound care includeduplex ultrasound (for example, as described in EP0814700 A1),transcutaneous oxygen monitoring (TCOM or TcPO₂) (for example, asdescribed in WO1980002795 A1), and skin perfusion pressure (SPP) (forexample, as described in CA2238512 C), each of which suffer severedisadvantages that limits their effectiveness in administering the righttherapy to chronic wound patients. Duplex ultrasound only measures bloodflow in large vessels (>1.5 mm). TCOM measurements are not optimallycorrelated with the status of the wound (Wounds 2009; 21(11):310-316).This is especially so as TCOM measurements are influenced by manyfactors including local edema, anatomical localization, thickness of theepidermal stratum corneum, and leg dependency (Figoni et al, J. RehabResearch Development 2006; 43 (7) 891-904). In addition, test resultsare heavily affected by moisture and temperature levels (Podiatry Today2012; 25(7) 84-92). Lo et al. (Wounds 2009:21(11) 310-316) report thatskin perfusion pressure (measured by laser Doppler) appears to be a moreaccurate predictor of wound healing versus TcPO₂; however SPP is onlyable to provide data at limited depth and requires skin temperature tobe normalized to 44° C., is sensitive to skin pigmentation andunreliable with edema.

Most recently, the use of diffuse speckle contrast analysis (DSCA) hasbeen developed to measure real-time blood perfusion in tissue depths ofup to two centimeters (2 cm), in absolute BFI (“blood flow index”) units(as described in more detail in U.S. Provisional App. Nos. 61/755,700,filed Jan. 23, 2013, and 61/830,256, filed Jun. 3, 2013, each of whichare hereby incorporated by reference in their entirety). The presentdisclosure centers on the use of DSCA to generate additionalinformation, such as low frequency oscillation data that forms the basisof a calibrated index that can guide clinical decisions in treatingischemia.

SUMMARY

Disclosed herein is a system for assessment of peripheral blood flowduring peripheral vascular intervention, the system including: a supportstructure configured to be positioned onto a patient's foot; a diffuseoptical flow (DOF) sensor carried by the support structure; an analyzerconfigured to analyze data from the DOF sensor to determine absoluteand/or relative blood flow at a location near the DOF sensor when thesupport structure is positioned onto a patient's foot; and a feedbackdevice configured to provide a signal indicative of the absolute and/orrelative blood flow determined by the analyzer.

In some embodiments, the support structure can include a retention ringand an adhesive material, or simply an adhesive material. In someembodiments, the support structure can include a strap having the DOFsensor attached thereto. In some embodiments, the DOF sensors can bearranged such that when the support structure is positioned onto thepatient's foot, at least two of the DOF sensors are over differenttopographical locations in the foot including different pedalangiosomes. In some embodiments, the DOF sensors can be arranged suchthat when the support structure is positioned onto the patient's foot,at least five of the DOF sensors are over different topographicallocations in the foot including different pedal angiosomes. In someembodiments, the analyzer can include a software autocorrelator. In someembodiments, the analyzer can include a hardware autocorrelator. In someembodiments, the signal indicative of the absolute and/or relative bloodflow can be visual, audible, or tactile. In some embodiments, the systemcan be configured to provide the signal indicative of the absoluteand/or relative blood flow in substantially real-time. In someembodiments, the system can be configured to provide the signalindicative of the absolute and/or relative blood flow within 1 secondfrom measurement.

Also disclosed herein is a method for real-time assessment of peripheralblood flow during peripheral vascular intervention procedures, themethod including: disposing at least one diffuse optical flow (DOF)sensor adjacent to a location on a foot of a patient; obtainingmeasurements of intensity fluctuation from the DOF sensor; analyzing theobtained measurements to determine an absolute and/or relative bloodflow rate at the location; and signaling the determined absolute and/orrelative blood flow rate to an operator.

In some embodiments, disposing the at least one DOF sensor can includeplacing a support structure onto the foot of the patient, the DOF sensorbeing carried by the support structure. In some embodiments, the methodcan further comprise disposing a plurality of DOF sensors adjacent to arespective plurality of locations on the foot of the patient. In someembodiments, the plurality of locations can include at least two, three,four, five, or more locations corresponding to different topographicallocations in the foot including different pedal angiosomes. In someembodiments, plurality of locations can include at least five locationscorresponding to five different topographical locations in the footincluding different pedal angiosomes. In some embodiments, signaling caninclude providing visual, audible, or tactile indicia of absolute and/orrelative blood flow. In some embodiments, signaling the determinedabsolute and/or relative blood flow rate to an operator can be performedin less than 1 second from measurement.

Further disclosed is a method for assessment of peripheral blood flowduring peripheral vascular intervention procedures, the methodincluding: disposing a plurality of diffuse optical flow (DOF) sensorsadjacent to a respective plurality of locations on an extremity of apatient, wherein at least two of the locations correspond to differenttopographical locations in the foot including different pedalangiosomes; determining an absolute and/or relative blood flow rates ateach of the plurality of locations in the extremity of the patient; andsignaling the determined absolute and/or relative blood flow rates to anoperator.

In some embodiments, the extremity can be a foot. In some embodiments,the extremity can be a hand. In some embodiments, the signaling can beperformed in substantially real-time. In some embodiments, thedetermined absolute and/or relative blood flow rates can be utilized toassess the efficacy of an interventional procedure.

Also disclosed herein is a patient interface, for supporting a pluralityof diffuse optical flow (DOF) sensors in optical communication with apatient's foot, comprising: a support, configured to be mountable on andcarried by the foot; at least three sensors carried by the support, eachsensor corresponding to a separate topographical location in the footincluding an angiosome selected from the group consisting of: theangiosome of the medial plantar artery; the angiosome of the lateralplantar artery; the angiosome of the calcaneal branch of the posteriortibial artery; the angiosome of the calcaneal branch of the peronealartery; and the angiosome of the dorsalis pedis artery.

In some embodiments, the patient interface can include at least foursensors carried by the support, each sensor corresponding to a separatetopographical location in the foot including a pedal angiosome. In someembodiments, the support can comprise a retention ring and adhesivematerial. In some embodiments, the support can comprise an opticalsource fiber and an optical detector fiber. In some embodiments, theoptical source fiber and the optical detector fiber can further compriseat least one coupling for releasably coupling the sensor to an analyzer.In some embodiments, the patient interface can comprise a cable, whichincludes a plurality of pairs of source fibers and detector fibers, eachpair connected to a separate sensor. In some embodiments, each sensorcan be releasably carried by the support.

Also disclosed herein is a system for assessment of peripheral bloodperfusion, the system including: a support structure configured to bepositioned onto a patient's foot; a diffuse optical sensor carried bythe support structure; an analyzer configured to analyze data from thediffuse optical sensor to characterize the composition or flow of bloodat a location near the diffuse optical sensor when the support structureis positioned onto a patient's foot; and a feedback device configured toprovide a signal indicative of composition or flow of blood determinedby the analyzer.

Further disclosed herein is a method for real-time assessment ofperipheral blood, the method including: disposing at least one diffuseoptical sensor adjacent to a location on a foot of a patient; obtainingmeasurements of diffused light; analyzing the obtained measurements tocharacterize the composition and/or flow rate of blood at the location;and signaling the determined composition and/or flow rate to anoperator. In some embodiments, sensors disclosed herein do not takepressure measurements, e.g., blood pressure measurements.

Also disclosed is a method for assessment of peripheral blood flowduring peripheral vascular intervention procedures, the methodincluding: disposing a plurality of diffuse optical sensors adjacent toa respective plurality of locations on an extremity of a patient,wherein at least two of the locations correspond to differenttopographical locations in the foot including different pedalangiosomes; characterizing the composition and/or blood flow rates ateach of the plurality of locations in the extremity of the patient; andsignaling the composition and/or blood flow rates to an operator.

Further disclosed herein is a patient interface, for supporting aplurality of diffuse optical sensors in optical communication with apatient's foot, comprising: a support, configured to be mountable on andcarried by the foot; at least three sensors carried by the support, eachsensor corresponding to a separate topographical location in the footincluding angiosome selected from the group consisting of: the angiosomeof the medial plantar artery; the angiosome of the lateral plantarartery; the angiosome of the calcaneal branch of the posterior tibialartery; the angiosome of the calcaneal branch of the peroneal artery;and the angiosome of the dorsalis pedis artery.

Disclosed herein is a system for using Low Frequency Oscillation Index(“LFI”) in blood flow measurements as a diagnostic index for ischemictissue management. In some embodiments, blood perfusion can be measuredas a function of time to provide time series data. Measurement of bloodperfusion can be accomplished by a number of different techniques,including, without limitation, diffuse correlation spectroscopy (DCS),diffuse speckle contrast analysis (DSCA), diffuse optical tomography,near-infrared spectroscopy, or Doppler flowmetry. In some embodiments,blood perfusion can be measured by non-optical techniques, for examplevia electrical or magnetic blood flow measurement techniques. In someembodiments, blood perfusion can be measured at a depth of at leastabout 1 mm below the skin. In some embodiments, blood perfusion can bemeasured at a depth of at least about 3 mm below the skin. In someembodiments, blood perfusion can be measured at a depth of at leastabout 5 mm below the skin. The tissue of interest can generally be inthe lower limbs especially if the system is used to assess peripheralvascular disease. In other applications, the tissue of interest may besurgical tissue flaps used in plastic and reconstructive surgery.

The obtained time series data may then be analyzed to obtain relevantparameters for use in clinical applications. For example, the timeseries data may be transformed into a power spectrum. In someembodiments, a Fourier transform may be used to transform the timeseries data into a power spectrum. In some embodiments, a Fast FourierTransform may be used. In other embodiments, a wavelet transform can beused to obtain the power spectrum.

Once the power spectrum is obtained, one or more parameters may becalculated and used to guide clinical judgment. In some embodiments,parameters can be calculated from the power spectrum over a specificfrequency range. In some embodiments, the frequency range can be betweenabout 0.001 Hz to about 1000 Hz, between about 0.001 Hz and about 0.1Hz, between about 0.045 Hz and about 0.01 Hz, or between 0.001 Hz toabout 0.045 Hz. The calculated parameter can be the area under the curveof the power spectrum within the specified frequency range. In someembodiments, the calculated parameter can be the local maximum power ofthe power spectrum within the specified frequency range.

In other embodiments, the calculated parameter may be the Pearsoncorrelation coefficient calculated between the time-series data of bloodflow obtained from at least two locations on the patient. In someinstances, these two locations may be the calcaneal and the armrespectively. In other instances, the locations may be the medialplantar and the arm.

In still another embodiment, the calculated parameter may be the Pearsoncorrelation coefficient calculated between the frequency domain spectrumobtained from at least two locations on the patient

In some embodiments, the calculated parameter may be the relative power.

In some embodiments, the calculated parameters may be processed by aState Vector Machine (SVM).

The calculated parameter may then be used for any one of a number ofclinical evaluations. For example, the calculated parameter can be usedto distinguish between healthy and ischemic limbs, such as healthy andischemic feet. The parameter may be used in some embodiments to identifypatients who may have endothelial or other vascular dysfunction that mayimpact wound healing. In some embodiments, the calculated parameter canbe used to screen claudicant patients for interventional therapy. Insome embodiments, the calculated parameter can be used to predict thelikelihood of success for conservative wound therapy. In someembodiments, the calculated parameter can be used to determine the needfor advanced wound therapy or interventional procedures, such as balloonangioplasty or vascular surgery. The calculated parameter may also beused in some embodiments to predict the likelihood of success of anamputation site healing. In some embodiments, the calculated parametercan be used to predict the likelihood of success of a hyperbaric oxygentherapy for chronic wound healing. In some embodiments, the calculatedparameter can be used to predict the likelihood of success of surgicalflaps. In some embodiments, the calculated parameter can be used topredict the uptake of drugs.

The measurement can be obtained locally, such as at the target site suchas on the patient's foot. In some embodiments, mathematicallytransforming the time-series data and/or calculating a parameter canalso be conducted locally. In some embodiments, the mathematicaltransform and/or calculating a parameter can be conducted remotely fromthe measurement site. For example, the measurement may be obtainedlocally, and the obtained time-series data may be transmitted to aremote location for further processing. In some embodiments, this canenable remote monitoring of a patient. Time series data can be obtainedby a probe worn by the patient, while the monitoring physician or otherindividual can be located remotely, and can receive the obtained timeseries data for further processing and evaluation. In variousembodiments, the processing (e.g., mathematical transform andcalculation of parameters) can be conducted in software, in hardware, orsome combination thereof. In some embodiments, the processing can beconducted on a local device such as a general purpose computer, while inother embodiments the processing can be conducted via a distributednetwork.

Also disclosed herein are systems for discriminating between at least afirst population and a second population. The systems can include one ormore of a processor configured to receive blood perfusion measurementsas a function of time to obtain time series data; mathematicallytransform the time series data into a power spectrum; calculate at leastone parameter of the power spectrum within a specific frequency range;and/or use the at least one calculated parameter as a discriminator forthe first population and the second population. The first population andthe second population can comprise two patient populations, such as, forexample, a healthy control group and an ischemic population. The systemcan also include at least one optical and/or non-optical sensorconfigured to measure blood perfusion as a function of time. The opticalsensor can include a diffuse optical flow sensor. The processor can beconfigured to mathematically transform the time series data into a powerspectrum using a Fourier transform, a fast Fourier Transform, or awavelet transform. The specific frequency range can be, for example,between about 0.001 Hz and about 1000 Hz, between about 0.001 Hz andabout 0.1 Hz, between about 0.045 Hz and about 0.1 Hz, or between about0.001 Hz and 0.045 Hz. The parameter could be, for example, an areaunder the curve of the power spectrum within the specific frequencyrange, or the local maximum power of the power spectrum within thefrequency range of interest.

Also disclosed herein is a method for discriminating between at least afirst population and a second population. The method can include thesteps of: measuring blood perfusion as a function of time to obtain timeseries data; calculating statistical parameters from the time seriesdata; and using at least one of the statistical parameters as adiscriminator for the first population and the second population.

In some embodiments, various statistical parameters can be determinedfrom data obtained relevant to blood flow of one, two, or more patientsor patient populations, including one or more of a standard deviation, amean, a median, a mode, a correlation coefficient, a linear regression,a Z score, a p value, a Chi-Squared test, and a Fisher's exact test.

Blood flow can be measured using optical (e.g., diffuse optical) and/ornon-optical flow sensors. Systems are also disclosed for discriminatingbetween at least a first population and a second population. The systemscan include a processor module configured to receive blood perfusionmeasurements as a function of time to obtain time series data; calculateat least one statistical parameter from the time series data; frequencyrange; and use the at least one calculated parameter as a discriminatorfor the first population and the second population. The sensors can beconfigured to send blood perfusion measurements through a wired orwireless connection to the processor.

Also disclosed herein are computer-implemented methods fordiscriminating between at least a first population and a secondpopulation. The methods can include any number of the following steps:sensing blood flow rate at a first anatomical location; sending datarelating to the blood flow rate to a module; sensing blood perfusion ata second anatomical location; determining a second blood flow index atthe second anatomical location; calculating the ratio of the first bloodflow index to the second blood flow index; and determining whether theratio corresponds to a characteristic of the first population or thesecond population by comparing the ratio to a predetermined thresholdvalue. The method can also include the step of providing a signal to anoperator related to the ratio. The first anatomical location can be thefoot, and the second anatomical location can be a location that is notdirectly perfused by an artery of the foot. The second anatomicallocation can be selected from the group consisting of: the thumb, theearlobe, the upper arm, and the thenar eminence. The first populationcan be, for example, an ischemic population, and the second populationcan be, for example, a non-ischemic population.

Also disclosed herein is a system for discriminating between at least afirst population and a second population. The system can include aprocessor configured to perform one or more of the following steps:receive blood perfusion data from a first sensor at a first anatomicallocation; determine a first blood flow index at the first anatomicallocation; receive blood perfusion data from a second sensor at a secondanatomical location; determine a second blood flow index at the secondanatomical location; calculate the ratio of the first blood flow indexto the second blood flow index; and determine whether the ratiocorresponds to a characteristic of the first population or the secondpopulation by comparing the ratio to a predetermined threshold value.The system can also include the first sensor configured to obtain bloodperfusion data from a first anatomic location, and the second sensorconfigured to obtain blood perfusion data from the second anatomiclocation.

Also disclosed herein is a computer-implemented method fordiscriminating between at least a first population and a secondpopulation. The method can include any number of the following steps:sensing a blood flow rate at a first anatomical location; sending datarelating to the blood flow rate to a module configured to analyze thedata relating to the blood flow rate; calculating a numerical valuederived from the data relating to the blood flow rate; determiningwhether the calculated value corresponds to a characteristic of thefirst population or the second population by comparing the ratio to apredetermined threshold value; and providing a signal to an operatorrelating to the calculated value. The first anatomical location can bethe foot. Calculating the numerical value can comprise calculating astatistical parameter from the data relating to the blood flow rate, orcharacterizing the blood flow rate as a function of a specified timeinterval. The statistical parameter can be, for example, a standarddeviation. The method can also include calculating the numerical valuecomprises calculating a power spectrum parameter from the data relatingto the blood flow rate, or calculating a ratio derived from the datarelating to the blood flow rate.

Also disclosed herein is a system for discriminating between at least afirst population and a second population. The system can include anynumber of the following: a module configured to receive blood flow ratedata from a first sensor at a first anatomical location; calculate anumerical value derived from the data relating to the blood flow rate;determine whether the calculated value corresponds to a characteristicof the first population or the second population by comparing the ratioto a predetermined threshold value; and provide a signal to an operatorrelating to the calculated value.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates the pedal angiosomes.

FIG. 1B illustrates five measurement points on the foot, eachcorresponding to one of the angiosomes shown in FIG. 1A.

FIG. 1C illustrates the branching of the arteries supplying the pedalangiosomes.

FIGS. 1D-1H illustrate measurement using diffuse optical flow (DOF)sensors at each of the five measurement positions of FIG. 1B.

FIG. 2 is a block diagram of a system for measuring flow of turbidmedia.

FIG. 3 is a schematic illustration of diffuse light penetration anddetection in multi-layer tissue.

FIG. 4 is a graph of autocorrelation functions for different flow rates.

FIG. 5A is a graph of two blood flow indices (BFIs) during cuffocclusion protocol.

FIG. 5B is a graph of autocorrelation functions illustrating thederivation of the two BFIs of FIG. 5A.

FIG. 6 is a graph of two BFIs during cuff occlusion protocol.

FIG. 7 illustrates various elements of a perfusion monitoring system,according to some embodiments.

FIG. 7A illustrates an embodiment of a DSCA perfusion monitor consoleand instrumentation box.

FIG. 7B illustrates embodiments of low-profile sensors.

FIG. 8A shows the raw BFI data (raw time series BFI data) measured atthe medial plantar section of the foot of two individuals, one healthyversus one with indications of limb ischemia, while FIG. 8B shows theequivalent power spectrum data of the same individuals (Fouriertransform of raw time series BFI data.

FIGS. 9A and 9B illustrate boxplots of low frequency oscillation index(LFI) from 26 healthy and 26 ischemic patients, assessed in twodifferent methods: maximum-based (LFI_(M)) in FIG. 9A, and area-based(LFI_(A)) in FIG. 9B.

FIGS. 10A and 10B illustrate receiver operating characteristic (ROC)curves for LFI_(M) and LFI_(A) respectively measured in the medialplantar (MP) region.

FIG. 11 illustrates a ROC curve for a 5-dimensional SVM utilizingpatient BFI input parameters.

FIG. 12 shows The Flow Transform Level (FTL) relating to the time seriesBFI, e.g., derivation of FTL from time series DSCA blood flow index(BFI) data, where intensity is measured at a frame rate of 60 Hz.

The standard deviation of 5 minutes of Medial Plantar BFI data sampledat 1 Hz and 2 Hz was calculated, and the resulting ROC curves are shownin FIGS. 13A and 13B. FIG. 13A illustrates the ROC of Standard Deviationof BFI @ 1 Hz; FIG. 13B illustrates the ROC of Standard Deviation of BFI@ 2 Hz.

The Standard Deviation of BFI from calcaneal and arm also showssignificant difference between healthy and ischemic patients, but notstrongly as with the medial plantar. The p-values of three positions arecompared in FIGS. 14A-14C, which are box plots of FTLs in the medialplantar, calcaneal, and arm regions, respectively. FIG. 14D illustratesFTL values for a number of patients including healthy and ischemicpatient populations.

FIG. 15A is a schematic illustration of a side-firing DOF sensor.

FIG. 15B illustrates a cover sock.

FIG. 15C illustrates a cover sock having a plurality of embeddedside-firing DOF sensors.

FIG. 15D illustrates another embodiment of a DOF sensor, with aretention ring and adhesive material.

FIG. 15E illustrates a detail view of the DOF sensor head shown in FIG.15D.

FIG. 16 is a flow diagram of a method for analyzing absolute and/orrelative blood flow.

FIGS. 17A-17C illustrate an embodiment of a DOF sensor, with ahorizontal sensor head.

FIGS. 18A-18D illustrate another embodiment of a DOF sensor with ahorizontal sensor head.

FIG. 19 illustrates a DOF sensor attached to a patient's foot.

FIG. 20 illustrates a DOF sensor attached to a patient's hand.

DETAILED DESCRIPTION Diffuse Optical Flow Sensors

A number of techniques exist for characterizing blood flow (which mayalso be referred to herein as blood perfusion), relying on measuring ofdiffusion of light. Such techniques include Diffuse CorrelationSpectroscopy (DCS) and Diffuse Speckle Contrast Analysis (DSCA). BothDCS and DSCA can be used to measure relative and/or absolute blood flow.Other techniques rely on measuring diffusion of light to detect othercharacteristics of tissue, such as biochemical composition,concentrations of oxyhemoglobin and deoxyhemoglobin, etc. Suchtechniques include Diffuse Optical Spectroscopy (DOS), Diffuse OpticalTomography (DOT), and Near-Infrared Spectroscopy (NIRS).

As used herein, “diffuse optical sensor” includes any sensor configuredto characterize properties of blood in tissue via measurement of diffuselight. As such, diffuse optical sensors include DCS, DSCA, DOS, DOT, andNIRS sensors. As used herein, the term “diffuse optical flow sensor”includes any sensor configured to characterize blood flow in tissue. Assuch, diffuse optical flow (DOF) sensors include both DCS and DSCAsensors.

Near-infrared diffuse correlation spectroscopy (DCS) is an emergingtechnique for continuous noninvasive measurement of blood flow inbiological tissues. In the last decade or so, DCS technology has beendeveloped to noninvasively sense the blood flow information in deeptissue vasculature such as brain, muscle, and breast. In contrast tosome other blood flow measurement techniques, such as positron emissiontomography (PET), single photon emission computed tomography (SPECT),and xenon-enhanced computed tomography (XeCT), DCS uses non-ionizingradiation and requires no contrast agents. It does not interfere withcommonly used medical devices such as pacemakers and metal implants. Ittherefore has potential in cancer therapy monitoring and bedsidemonitoring in clinical settings.

A DCS system can include a light source such as a laser with a longcoherence length, a detector such as a photon-counting avalanchephotodiode (APD) or photomultiplier tube (PMT), and an autocorrelator.In various embodiments, the autocorrelator may take the form of hardwareor software. As one of the central components of the DCS system, theautocorrelator computes the autocorrelation function of the temporalfluctuation of the light intensity obtained from the detector.

However, DCS can suffer from a long integration time, high cost, and lowchannel number of simultaneous measurements. One factor contributing tothese limitations is dependence on very sensitive photodetector(s) andsubsequent autocorrelation calculation. Diffuse Speckle ContrastAnalysis (DSCA) is a newer technology that provides an improvedflowmetry system enabling cost-effective, real-time measurements usingstatistical analysis without having to rely on autocorrelation analysison fast time-series data. This statistical analysis can be implementedeither in spatial domain using a multi-pixel image sensor, or in thetime domain using slow counter. A multi-pixel image sensor can also beused for time domain analysis such that single or multiple pixels act asan individual detector, which is especially suitable for multi-channelapplication. In various embodiments, this approach can be used tomeasure blood flow, whether absolute, relative, or both.

DSCA can be implemented in both spatial and time domains. For spatialDSCA (sDSCA), a raw speckle image is first obtained from the samplesurface. The raw speckle images may first be normalized by the smoothintensity background, which can be averaged over a number of speckleimages. The speckle contrast, K_(s) is defined as the ratio of thestandard deviation to the mean intensity across many detectors orpixels, K_(s)=σ_(s)/<I>, where subscript s refers to the spatial, asopposed to temporal, variations. The quantity K_(s) is related to thefield autocorrelation function g₁(τ) as follows:

${V(T)} = {\lbrack {K_{s}(T)} \rbrack^{2} = {\frac{2}{T}{\int_{0}^{T}{{( {1 - {\tau/T}} )\lbrack {g_{1}(\tau)} \rbrack}^{2}d\; \tau}}}}$

where V is the intensity variance across the image, and T is the imagesensor exposure time. By using the known solution of the correlationdiffusion equation in the semi-infinite medium, the formal relationshipbetween the flow rate and K_(s) can be derived. The relationship betweenthe flow and 1/K_(s) ² turns out to be substantially linear in the rangeof flow seen in body tissue, with 1/K_(s) ² increasing with increasingflow rate.

Another way to implement this speckle contrast rationale for flowmetryis to use statistical analysis on time series data obtained byintegrating over a certain time. This temporal domain analysis isreferred to herein as tDSCA. The integrating time for tDSCA can beregarded as analogous to the exposure time of the image sensor in sDSCA.In the case of tDSCA, a detector with moderate sensitivity with anintegrating circuit can be used. For example, each pixel on a CCD chipcan be used for this purpose as each CCD pixel keeps accumulatingphotoelectrons for a given exposure time. Therefore, a number ofsingle-mode fibers can be directly positioned on some locations on asingle CCD chip, resulting in a multi-channel tDSCA system withoutlosing any time resolution. The number of channels is only limited bythe CCD chip size, pixel size, and the area of each fiber tip. In someembodiments, tDSCA can use sensitive detectors such as avalanchephotodiode (APD) and/or photomultiplier tube (PMT) with a slow countersuch as a counter included in a DAQ card with USB connection, butscaling this embodiment to multichannel instrument is costly and bulky.Time-series data taken either way can be obtained by repeatmeasurements, for example 25 measurements can be made consecutively,after which the data can be analyzed statistically to determine the flowrate. In a configuration with an exposure time of 1 ms, one flow indexwould be obtained every 25 ms, resulting in approximately 40 Hzoperation.

The statistical analysis of the time-series data can be substantiallyidentical to that described above with respect to sDSCA, except that thestatistics (average intensity and standard deviation of intensity) arecalculated in the time domain, rather than the spatial domain. As aresult, tDSCA may provide lower time resolution than sDSCA. However, thedetector area for tDSCA may be significantly smaller than with sDSCA. Aswith the spatial domain counterpart, tDSCA provides an approach withinstrumentation and analysis that are significantly simpler and lesscomputationally intensive than traditional DCS techniques.

Both DCS and DSCA technology can be used to evaluate on a real-timebasis the absolute and/or relative blood flow in the foot, therebyproviding an important tool for interventional radiologists and vascularsurgeons treating ischemia in the foot. With current tools in theoperating room, the physician can usually assess via X-ray fluoroscopywhether an intervention such as a balloon angioplasty procedure hassucceeded in opening up and achieving patency of a limb artery. However,the clinical experience has been that structural patency as observedwith fluoroscopy is not a reliable indicator of successful reperfusionof the topographical region of the foot where the ulcer wound, ischemictissue (e.g., blackened toes) or other clinical manifestation islocated. To augment fluoroscopic data on arterial patency, a pluralityof DOF sensors used in either DCS or DSCA systems can be positioned atdifferent topographical regions of the foot to assess absolute and/orrelative blood flow in the different regions. For example, thetopographical regions may correspond to different pedal angiosomes.

An angiosome is a three-dimensional portion of tissue supplied by anartery source and drained by its accompanying veins. It can includeskin, fascia, muscle, or bone. Pedal angiosomes are illustrated in FIG.1A. Below the knee, there are three main arteries: the anterior tibialartery, the posterior tibial artery, and the peroneal artery. Theposterior tibial artery gives at least three separate branches: thecalcaneal artery, the medial plantar artery, and lateral plantar artery,which each supply distinct portions of the foot. The anterior tibialartery supplies the anterior ankle and continues as the dorsalis pedisartery, which supplies much of the dorsum of the foot. The calcanealbranch of the peroneal artery supplies the lateral and plantar heel. Theanterior perforating branch of the peroneal artery supplies the lateralanterior upper ankle. As a result, the pedal angiosomes include: theangiosome of the medial plantar artery, the angiosome of the lateralplantar artery, the angiosome of the calcaneal branch of the posteriortibial artery, the angiosome of the calcaneal branch of the peronealartery, the angiosome of the dorsalis pedis artery. There is some debateas to whether there is a separate sixth pedal angiosome corresponding tothe anterior perforating branch of the peroneal artery.

FIG. 1B illustrates five measurement points on the foot, eachcorresponding a pedal angiosome identified in FIG. 1A. By detectingblood flow in each of these positions, blood flow from the variousarteries can be evaluated independently. For example, measurement ofblood flow at point A (see FIG. 1D) is indicative of blood flow from thedorsalis pedis artery, and also the anterior tibial artery. Similarly,measurement of blood flow at point B (see FIG. 1E) corresponds to themedial plantar artery, while point C (see FIG. 1F) corresponds to thelateral plantar artery, point D (see FIG. 1G) corresponds to thecalcaneal branch of the posterior tibial artery, and point E (see FIG.1H) corresponds to the calcaneal branch of the peroneal artery.

FIG. 1C is a branching diagram of the arteries supplying the pedalangiosomes. The blood flow measurement points A-E are illustrated asterminating respective artery branches, though in practice themeasurement points need not be at the distal-most end of the respectivearteries. As noted above, measurements at any of the points A-E mayprovide valuable clinical information regarding local perfusion.

Topographical-based peripheral vascular interventions, such asangiosome-directed peripheral vascular interventions, have beendeveloped relatively recently, and show promising performance comparedwith traditional intervention, particularly in terms of improved limbsalvage rates. A system employing a plurality of DOF sensors can providereal-time feedback on changes in perfusion of different topographicallocations in the foot, e.g. angiosome by angiosome, so thatinterventional radiologists or vascular surgeons may immediatelyevaluate whether specific intervention at a target artery has succeededin restoring sufficient blood perfusion to the targeted topographicalregion of the foot where the ulcer wound, ischemic tissue or otherclinical manifestation is located.

FIG. 2 is a block diagram of a system for measuring flow of turbidmedia. A sample 102 includes a heterogeneous matrix therein. Within thismatrix is an embedded flow layer with randomly ordered microcirculatorychannels through which small particles 207 move in a non-orderedfashion. For example, in some embodiments the sample may be body tissue,with a complex network of peripheral arterioles and capillaries. Asource 108 injects light into the sample 102. A detector 110 can detectlight scattered by the moving particles 207 in the microcirculatorychannels. The detector 110 can be positioned to receive light thatpasses from the source into the sample, and diffuses through the sample.In some embodiments, the detector can be coupled to the sample by asingle-mode optical fiber. In some embodiments, the detector may be amulti-pixel image sensor, for example a CCD camera, used to image anarea of the sample. In other embodiments, the detector may be aphoton-counting avalanche photodiode (APD) or photomultiplier tube(PMT). As the particles flow in random direction, the scattering oflight from the source 108 will vary, causing intensity fluctuations tobe detected by the detector 110.

An analyzer 112 is coupled to detector 110 and configured to receive asignal from the detector 110. The analyzer 112 may comprise anautocorrelator, which measures the temporal intensity autocorrelationfunction of light received by the detector 110. The autocorrelationfunction can be used to obtain the scattering and flow characteristicsof the small particles flowing in the sample 102. The time-dependentintensity fluctuations reflect the time-dependent density fluctuationsof the small particles 207, and accordingly the autocorrelation functioncan be used to determine the flow rate within the sample 102. In someembodiments, a hardware autocorrelator may be employed, while in otherembodiments a software autocorrelator can be used. The flow rate orother characteristic determined by the analyzer 112 may be outputted toa display 114. The measured quantity may therefore be provided to anoperator via the display 114. In various embodiments, the operator maybe a clinician, diagnostician, surgeon, surgical assistant, nurse, orother medical personnel. In some embodiments, the measurement may beprovided via display 114 in substantially real-time. In someembodiments, the measurement may be provided via display 114 withinabout 1 second from measurement, e.g., within about 1 second of the timethat the scattered light is detected by the detector, the measurementmay be provided via display 114. In various embodiments, the measurementmay be provided within less than about 10 minutes, within less thanabout 5 minutes, within less than about 1 minute, within less than about30 seconds, within less than about 10 seconds, or within less than about1 second from detection.

In some embodiments, as noted above, a software autocorrelator may beused. This may advantageously provide additional flexibility comparedwith a hardware autocorrelator, as it allows for data pre-processing. Asoftware autocorrelator may also reduce the cost of a DCS system, whilealso reducing size and improving form factor. The ability to pre-processdata can also improve the accuracy of measurements.

FIG. 3 is a schematic illustration of diffuse light penetration anddetection in multi-layer tissue. As illustrated, a source 202 and adetector 204 are both positioned adjacent a portion of tissue 206. Asnoted above, in some embodiments optical fibers may be used to coupleone or both of the source and detector to the tissue. The tissue 206 ismulti-layer, including an upper layer 208 with no flow, and a deeperlayer 210 with flow. A plurality of light-scattering particles 212 flowwithin capillaries in flow layer 210, and may include, for example, redblood cells. As light 214 is emitted from the source 202, it diffuses asit penetrates the tissue 206. As illustrated, a portion of the light 214is diffused such that it is incident on the detector 204. The light 214may follow a roughly crescent-shaped path from the source 202 to thedetector 204. The depth of penetration of the light 214 detected by thedetector 204 depends on the separation between the source and thedetector. As the distance increases, penetration depth generallyincreases. In various embodiments, the separation distance may bebetween about 0.5 cm and about 10 cm, or in some embodiments betweenabout 0.75 cm and about 5 cm. Preferably, in other embodiments theseparation distance may be between about 1 cm and about 3 cm. In variousembodiments, the separation distance may be less than about 10 cm, lessthan about 9 cm, less than about 8 cm, less than about 7 cm, less thanabout 6 cm, less than about 5 cm, less than about 4 cm, less than about3 cm, less than about 2 cm, less than about 1 cm, less than about 0.9cm, less than about 0.8 cm, less than about 0.7 cm, less than about 0.5cm, less than about 0.4 cm, less than about 0.3 cm, less than about 0.2cm, or less than about 0.1 cm. The penetration depth may vary, forexample in some embodiments the penetration depth of the sensor may bebetween about 0.5 cm and about 5 cm, or in some embodiments betweenabout 0.75 cm and about 3 cm. Preferably, in other embodiments thepenetration depth may be between about 5 mm and about 1.5 cm. Of course,the tissue optical properties of the various layers also contribute tothe penetration depth of the light, as does the intensity, wavelength,or other characteristics of the light source. These variations can allowfor the depth of measurement to be adjusted based on the part of thebody being analyzed, the particular patient, or other considerations.Measurements obtained by the detector 204 may then be processed andanalyzed to calculate the autocorrelation function. As seen in FIG. 4,the autocorrelation function may be used to determine the flow rate inthe tissue.

FIG. 4 is a graph of autocorrelation functions for different flow rates,with steeper decay of the autocorrelation curve indicating faster flowrates. The autocorrelation curves are plotted on a semi-logarithmicscale in the graph. As is generally known in the art, blood flow datacan be analyzed by fitting each autocorrelation curve to a model, such asemi-infinite, multi-layer diffusion model. The fitted autocorrelationcurves can then provide relative blood flow rates, which can be usefullyapplied during peripheral interventional procedures such as balloonangioplasty or surgery, or as a diagnostic tool.

Diffuse optical flow (DOF) sensors (which, as described above, caninclude either or both DCS and DSCA sensors) can be particularly usefulin measuring microcirculation, for example in measuring blood perfusionin the foot. This technique can be additionally improved by employingthe concept of pedal topography. One example of a topographical analysisof blood flow in the foot incorporates the concept of pedal angiosomes,as described above.

In many cases, prior to vascular intervention, an interventionalradiologist or vascular surgeon will image the vasculature of interest,for example using fluoroscopy, computed tomography, ultrasound, or otherimaging technique. With such imaging, several potential occlusions orlesions may be identified. Peripheral intervention, such as balloonangioplasty, atherectomy, or surgical bypass/grafts can be employed tore-open one or more of the identified occlusions or lesions (“the targetlesions”), in an effort to restore perfusion to the affected region(s)of the foot. For these peripheral interventions to result in successfullimb salvage, blood perfusion must reach a sufficient level that permitshealing of the foot wound. Without a real-time perfusion monitor, aphysician has no way of knowing for sure if an intervention has achievedan improvement in perfusion sufficient for wound healing, or at all. Theuse of real-time measurement of blood perfusion at various topographiclocations of the foot, as described herein, addresses this problem. Itprovides objective quantitative perfusion data in real-time so that thephysician can know with certainty whether a specific intervention at atarget lesion has succeeded in restoring perfusion to the topographicregion of the foot on which the wound is located. If a determination hasbeen made that an acceptable level of perfusion at the desiredtopographic region has been achieved, the physician can avoid theadditional risk associated with further intervention, and bring theprocedure to a close. Alternatively, if a specific intervention at atarget lesion has not resulted in any perfusion improvement as measuredby a real-time perfusion monitor, the physician will thereby be guidedto undertake the additional risk of proceeding onto secondary targetlesions. The use of a real-time perfusion monitor thus averts thesituation where a peripheral intervention procedure is ended prematurelyprior to achieving the desired improvement in perfusion. It also guidesphysicians as to which target lesion (when revascularized) resulted inthe greatest perfusion improvement at the desired topographic region ofthe foot. This real-time knowledge would in turn inform the physician asto the optimal placement for use of a drug-eluting balloon or othermeans to prolong the patency of the vessel in which the said lesion islocated.

Although changes in perfusion can be seen directly from the change inshape of the autocorrelation function, potentially more useful ways todefine a blood flow index (BFI), which may also be referred to herein asa blood perfusion index (BPI) have been developed. FIG. 5A is a graph oftwo such BFIs over time during a cuff occlusion protocol. The dashedvertical lines indicate the starting and stopping times of the cuffinflation. The top chart illustrates a BFI calculated from verticalcrossing of the autocorrelation curve, while the lower chart illustratesa BFI calculated from horizontal crossing of the autocorrelation curve.FIG. 5B is a graph illustrating these two different methods ofcalculating BFI. The solid line represents the zero flow reference data,while the dotted line represents real-time autocorrelation data. Thevertical crossing indicator compares the y-axis value (g₂) of thereal-time autocorrelation data and the reference data at a given time.For example, the first indicator can be calculated as 1/g₂ or 1.5-g₂.The horizontal crossing indicator compares the time difference betweenthe autocorrelation data and the reference data at a given flow rate.For example, the second indicator can be calculated as log(t2/t1).

Charts such as those shown in FIG. 5A, or other such indicia of bloodflow, can be displayed to an operator in real-time via audible, visual,or tactile feedback. A physician may thereby be provided withsubstantially real-time feedback on the efficacy of a peripheralintervention. For example, during balloon angioplasty, a physician canmonitor the BFI as measured on a specific location of the foot. The BFIwill decrease while the balloon is inflated, and increase afterdeflation. After repeated inflation of the balloon to perform theangioplasty, the BFI should increase relative to the pre-angioplastybaseline, indicating that the angioplasty procedure has resulted in animprovement in perfusion at the target foot tissue. A BFI that does notincrease relative to the pre-angioplasty baseline indicates that theballoon angioplasty was not successful in restoring perfusion. Providingsuch feedback in real-time is an enormous benefit to physiciansperforming vascular intervention. Rather than waiting post-operativelyfor hours or days to determine whether perfusion has been improved,during which time the foot may deteriorate to the point of requiringamputation, the use of DOF sensors at select pedal locations during theangioplasty procedure can provide immediate feedback, allowing thephysician to continue, modify, or conclude the procedure as needed. Asnoted above, in various embodiments, the feedback may be provided, insome cases, within less than about 10 minutes, within less than about 5minutes, within less than about 1 minute, within less than about 30seconds, within less than about 10 seconds, or within less than about 1second from measurement. In some embodiments, success of arevascularization procedure can be indicated by an increase in BFI ofabout or at least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%,55%, 60%, 65%, 70%, 75%, 80%, or more compared to a BFI value prior tothe procedure.

While the example above relates to balloon angioplasty, the use of DOFsensors to assess blood flow (whether relative, absolute, or both) inthe foot can be advantageously applied before, during, or after a numberof different interventions. For example, DOF sensors can be used to aidinterventions such as rotational atherectomy, delivery of lyticsubstances including but not limited to tPA, bypass procedures, stentand/or graft placement, or any other intervention.

In addition to the above-described real-time monitoring of bloodperfusion in the operating room, derivative indices based on the rawblood perfusion data generated via DCS or DSCA can also serve as toolsin an inpatient or outpatient setting, for example, to directappropriate wound or ulcer therapy based on the patient's level oftissue perfusion, or to screen for critical thresholds of peripheralarterial disease, by measuring blood perfusion in the extremities (e.g.the foot). Such derivative indices include the Foot Thumb Index (“FTI”),the Low Frequency Oscillation Index (“LFI”) and its two parameters of“LFI_(A)” and “LFI_(M)”, as well as the Support Vector Machines Index(“SVM”) and the Flow Transform Level (“FTL”). These derivative indicesare described below and will jointly be referred to as “the DerivativeIndices.” In some embodiments, the function of time references in one ormore of the derivative indices can be, for example, between about 15seconds and about 15 minutes, between about 30 seconds and about 5minutes, between about 30 second and about 2 minutes, or about 30seconds, 45 seconds, 1 minute, 1.5 minutes, 2 minutes, 2.5 minutes, 3minutes, 4 minutes, 5 minutes, 6 minutes, 7 minutes, 8 minutes, 9minutes, 10 minutes, or ranges involving any two of the foregoingvalues.

The Foot-Thumb Index (“FTI”)

As described elsewhere in this application, blood flow measurements maybe made via absolute BFI measurements, or relative BFI readings beforeand after an intervention, for example, before and after angioplasty,such as balloon angioplasty. However, an alternative measurement ofperfusion may be obtained by taking the ratio of the absolute BFI in thefoot to the absolute BFI in another reference location on the body suchas but not limited to the thumb, earlobe, upper arm (deltoid/shoulderregion), or palm (thenar eminence). For ease of reference, thisalternative measurement is referred to herein as the FTI (the Foot-ThumbIndex).

The FTI may address the difficulty in comparing absolute BFI readingsfrom one individual to another given the variability occasioned bydifferent physiological and environmental factors such as roomtemperature, skin/tissue temperature, hemoglobin concentration, time ofday, skin pigmentation, etc. It thus allows for calibration towards astandardized value or range of values that serves as basis fordifferentiating ischemic and non-ischemic tissue. This standardizationwould provide reference values for populations, both normal andabnormal, and do so without the need for standardized temperature orenvironmental pressure which is required for laser Doppler ortranscutaneous oxygen measurements.

With reference to FIG. 6, the upper chart illustrates the relative BFIchart reflecting perfusion in the medial plantar angiosome of the footof an individual undergoing a cuff occlusion, as measured by DSCA, withthe cuffed period marked out between the vertical lines. The lower chartillustrates the FTI or medial plantar BFI normalized against the thumbBFI of the same patient during the cuff occlusion process. As shown inFIG. 6, the unnormalized values reflect the absolute BFI readings whilethe FTI (normalized) value (between the two vertical markers) reflectsthe FTI during the cuffed period. The 0.91 listed in FIG. 6 refers tothe mean of the BFI prior to cuff occlusion (to the left of the firstvertical line).

In some embodiments, comparing the calculated FTI to a predeterminedthreshold value is utilized to discriminate between a first population(e.g., a population having a characteristic disease or risk factor forthe characteristic disease, e.g., an ischemic population) and a secondpopulation (e.g., a population not having a characteristic disease orrisk factor for that characteristic disease, e.g., a non-ischemicpopulation; or a different disease or risk factor for the differentdisease). In some embodiments, it can be determined that a subject fallsinto a characteristic population, e.g., an ischemic population, if theFTI is less than about 0.90, 0.88, 0.86, 0.84, 0.82, 0.80, 0.78, 0.76,0.74, 0.72, 0.70, 0.68, 0.66, 0.64, 0.62, 0.60, 0.58, 0.56, 0.54, 0.52,0.50, 0.48, 0.56, 0.54, 0.52, 0.50, 0.48, 0.46, 0.44, 0.42, 0.40, orless.

Low Frequency Oscillation Index (“LFI”)

LFI is a measurement index related to LFO, the low frequency oscillationobserved in hemodynamic measurements such as blood flow, oxygenation,volume and pressure. The current literature describes two differentorigins of LFO, namely those derived from Mayer waves and vasomotionwaves. Mayer waves are spontaneous oscillations in arterial pressure,which oscillations have significant correlation with oscillations ofsympathetic nerve activity. Vasomotion waves, on the other hand, areoscillations generated by the blood vessel walls. The key distinction isthat Mayer waves are driven by nerve activity while vasomotion waves area characteristic of the vessel wall's autonomic behavior which is notcorrelated to nerve activity.

Similar observations have been made in earlier studies, albeit with morerudimentary laser Doppler tools (Schmidt et al, J Vasc Surg 1993;18:207-15 and Stansberry et al, Diabetes Care; July 1996; 19, 7:715-21). In the context of blood flow, oscillations caused by vasomotioncan be measured by laser Doppler flowmetry, but only in small spatialscale. In clinical parlance, this means that laser Doppler cannotpenetrate beyond skin level to measure tissue perfusion at depth. Withthe development of diffuse speckle contrast analysis (DSCA) which canutilize a CCD as a detector to integrate transmitted light intensity,coupled with statistical analysis to retrieve minute blood flow data, itis now possible to overcome the limitations of laser Doppler to measureLFO at tissue depths up to two centimeters, or greater in someembodiments. Relative to laser Doppler, the tissue volume measured inDSCA is several orders of magnitude larger, and hence the observed LFOis much more sensitive to microcirculatory pathologies.

Studies by Rucker et al (Rucker et al in Am J Physiol Heart Circ, 2000)showed that under critical perfusion conditions (when arterial bloodsupply is reduced to the point of ischemia), it is the vasomotion andflow motion in the skeletal muscle that preserve nutritive function tosurrounding tissue like skin, subcutis and periosteum, which areincapable or less capable of this protective mechanism. In addition, theimpaired endothelial dysfunction as seen in diabetes directly impairsvasomotor function (Kolluru et al in Intl J of Vascular Med 2012)leading to delayed vascular re-modeling and wound healing. It followstherefore that measurement of either just partial pressure of oxygen(TcPO₂) or perfusion pressure in the skin alone (SPP) do not reflect thecritical nature of the ischemia in the underlying tissue to be able topredict wound healing accurately. LFO evaluation of deep tissueperfusion (e.g., up to 2 cm) is a direct measure of microvascularvasomotor function in tissue and is likely to be a superior predictor ofwound healing.

The impact of these underlying pathologies also explains the significantcorrelation between the Derivative Indices and ischemia. In healthypatients, there a larger deviation and variance in blood perfusion owingto healthier and more elastic vessels. Also, their cardiovascularfunction is likely to be stronger and as a consequence causing largervariation in blood perfusion. In contrast, patients with ischemia mayhave a variety of co-morbidities including diminished cardiac capacityand calcified microvasculature resulting in less exuberant bloodperfusion fluctuations.

As described in more detail above as well as in U.S. Pub. No.2014/0052006 A1, which is hereby incorporated by reference in itsentirety, optical measurement techniques such as diffuse specklecontrast analysis (DSCA) can be utilized to measure real-time bloodperfusion in tissue depths of up to two centimeters (2 cm), in absoluteBFI (“blood flow index”) units. The BFI readings, however, do notrepresent the full extent of the information that can be obtained fromthe raw data. In addition to BFI, it is possible to extract criticalinformation about the health of the microvascular blood vessels byevaluating characteristics of the BFI signal, including, but not limitedto, analyzing the BFI signal's power spectrum and statisticalcharacteristics.

DSCA Measurements in Healthy v. Ischemic Feet

The BFI measurement of blood perfusion was taken of 68 individuals fromtwo groups. The first group comprised 30 healthy volunteers, while thesecond group comprised 38 patients who sought treatment forclaudication, amputation follow-up or general podiatry. Of the healthyvolunteers, 4 were excluded due to BMI>28. For the patient group, 2 wereexcluded due to incorrect fiber connection of the equipment, 2 wereexcluded for known venous disease, and 8 were excluded due to normalreadings of ankle-brachial index (ABI) and/or toe-brachial index (TBI),coupled with physician assessment of a lack of clinical indicators ofischemia, or the presence of clinical indicators that pointed away fromischemia. The resulting data was thus based on a comparison of 26measurements in the healthy group, and 26 measurements in the patientgroup.

In the healthy group, there were 11 men plus 15 women, with an age rangebetween 22 to 46, and a median age of 31. In the patient group, therewere 14 men plus 12 women, with an age range between 53 and 82, and amedian age of 68.

Data Acquisition

In some embodiments, a system for blood flow assessment includes asupport structure configured to be positioned on an anatomical locationof a patient, one or more sensors carried by the support structure, ananalyzer configured to analyze data from the sensor(s) to determineabsolute and/or relative blood flow at a location near the sensor, and afeedback device configured to provide a signal indicative of theabsolute and/or relative blood flow determined by the analyzer. In someembodiments, as illustrated in FIG. 7, a system 700 may be arranged in adistributed configuration comprising at least two sub-sections such as aconsole 702 disposed on a movable cart, with an extension umbilicaladaptor 708 that is connected to the catheterization table 701. Theumbilical adaptor 708 will be configured to connect (e.g., via sensorleads 706) with patient contacting sensors 704, and can also connect viaconduit 707 to the console 702. Placing the umbilical adaptor 708 inproximity to the patient on the catheterization table 701 can simplifythe connection/disconnection of sensors 704 to/from the system, andsimplify the application of the sensors 704 to the patient. Any of theconnections described and illustrated can be wired or wirelessconnections. The umbilical adaptor 708 may be passive—providing only aremote connection point for patient contacting sensors 704; or may beactive—comprising active circuitry and optics, which may include (but isnot limited to) sensor detection, identification, authenticationhardware/software; contact verification hardware/software; one, two, ormore laser sources (e.g., 1, 2, 3, 4, 5, or more laser sources); one,two, or more photodetectors (e.g., 1, 2, 3, 4, 5, or morephotodetectors); CPU; display; touchscreen; keyboard/buttons;audio/visual annunciators; power source; data storage;wireless/wired/optical networking interfaces; input/outputconnectors/interfaces; gesture recognition interface, and the like. Theconsole 702 can include active circuitry and optics, which may include(but is not limited to) sensor detection, identification, authenticationhardware/software; contact verification hardware/software; one, two, ormore laser sources (e.g., 1, 2, 3, 4, 5, or more laser sources); one,two, or more photodetectors (e.g., 1, 2, 3, 4, 5, or morephotodetectors); CPU; display; touchscreen; keyboard/buttons;audio/visual annunciators; power source; data storage;wireless/wired/optical networking interfaces; input/outputconnectors/interfaces; gesture recognition interface, and the like.

The patient contacting sensors may be configured as single (one-time)use disposables or multiple use devices. Single use enforcement may beimplemented using methods including, but not limited to, time-limitedactivation based on unique serial numbers on packaging, procedurelimited activation based on embedded identification circuitry, frangibleconnectors, frangible patient contacting assemblies, light/time/airsensitive materials which degrade mechanically, chemically, oroptically, keyed resistance/impedance circuits, custom keyed connectors,or any anti-counterfeiting method that may be appropriately adapted forthis application, or any combination thereof.

The instrument used for measurement of BFI and LFO can be, for example,a DSCA perfusion monitor, which may be a 3-channel monitor in someembodiments. Each of the 3 channels can be connected via a laser fiberto a sensor comprising a laser source and detector. FIGS. 7A-7C show oneembodiment of the device and the low-profile sensors 1304 attached tothe foot via adhesive Tegaderm tape 1306 (3M, United States). FIG. 7Aillustrates an embodiment of the console 1300 and instrumentation box1302. FIG. 7B illustrates embodiments of low-profile sensors 1304.

Each volunteer/patient was asked to sit while sensor locations wereidentified on the medial plantar and calcaneal areas of the foot,avoiding calluses, and on the deltoid of the arm. Local temperaturereadings were taken at the medial plantar, calcaneal and deltoid using anon-contact dermal imager (Ti9, Fluke Corporation). One sensor was thenaffixed to each of the medial plantar, calcaneal and deltoid. Once thethree sensors were affixed, the BFI data was recorded for 5 minutes withthe patient in a seated position with both feet hanging down.Thereafter, the patient was asked to lie down in a supine position andthe BFI data was recorded for another 5 minutes. Finally, the sensorswere detached, and temperature on the three sites was taken one moretime. All readings were taken on the right side of the body, unless theright foot or forefoot had already been amputated, or where the patientpresented with clinical evidence of greater ischemia on the left leg ascompared to the right e.g. a chronic non-healing wound on the left foot,extreme claudication on the left leg with no symptoms on the right,angiographically defined vessel narrowing in the left limb vessels, etc.

Power Spectrum Analysis:

The 5 minute time-series BFI data of patients in supine positions at asampling frequency of 1 Hz (total data set was 300 points) wasnormalized by dividing it by its mean value, and then subjected to aFast Fourier Transformation to obtain the Power Spectrum.

By way of example, FIG. 8A shows the raw BFI data (raw time series BFIdata) measured at the medial plantar section of the foot of twoindividuals, one healthy versus one with indications of limb ischemia,while FIG. 8B shows the equivalent power spectrum data of the sameindividuals (Fourier transform of raw time series BFI data. Lowfrequency oscillation indices based on maximum peak signal (LFI_(M)) areshown with arrows).

There are several parameters/characteristics that can be obtained fromthe Power Spectrum, and a few examples are listed in the table below:

TABLE 1 Examples of parameters that can be derived/extracted from BFIPower Spectra. LFI_(M) The Low Frequency oscillation Index (Maximum)relates to the peak signal power in the frequency band between 0.045 Hzand 0.1 Hz LFI_(A) The Low Frequency oscillation Index (Area) relates tothe area under the normalized spectrum in the frequency band between0.045 Hz and 0.1 Hz Absolute Power: The Absolute Power of a frequencyband, P_(A)(f_(L), f_(H)), is defined as the signal power within thatspecific frequency band from f_(L) to f_(H). Mathematically: If a signalx(t) has Fourier transform X(f), its power spectral density is |X(f)|² =Sx(f). The absolute spectral power in the band of frequencies from f_(L)Hz to f_(H) Hz is given by Absolute Spectral Power in Band =∫_(fL)^(fH)Sx(f)df Relative Power: The Relative Power, P_(R)(f_(L),f_(H)), of a frequency band is defined as the ratio of the absolutepower within that specific frequency band (from f_(L) to f_(H)) dividedby the total signal power across the entire frequency spectrum. This isa dimensionless quantity. P_(R)(f_(L), f_(H)) = P_(A)(f_(L),f_(H))/P_(A)(0, ∞) Mathematically: The relative spectral power measuresthe ratio of the total power in the band (i.e., absolute spectral power)to the total power in the signal. i.e., Relative Spectral Power in Band=$\frac{\int_{fL}^{fH}{{{Sx}(f)}{df}}}{\int_{0}^{\infty}{{{Sx}(f)}{df}}}$Band-Pass Band-pass filtering refers to the processing the original timeCorrelation series data to extract signal components that exists withina specific Coefficient: frequency band. For example a 0.01 Hz to 0.1 Hzband-pass filter will only allow signal components between 0.01 Hz to0.1 Hz to pass; signal components frequencies lower than 0.01 Hz orhigher 0.1 Hz will be blocked. The Pearson correlation coefficientbetween two variables is defined as the covariance of the two variablesdivided by the product of their standard deviations. The result is anumber between +1 and −1, where 0 represents that there is nocorrelation, and +1 or −1 represent complete positive or negativecorrelations respectively. For example a correlation coefficient can becalculated between the time series BFI data from two separate anatomicalregions of the same patient (e.g. calcaneal BFI correlation with medialplantar BFI), which will provide a measure of how similar the twosignals are. pass filters are known, such as the 3rd order Butterworthfilter etc. The band-pass correlation coefficient of two signals is thePearson correlation coefficient calculated between two signals that haveundergone band-pass filtering. For example, one can calculate thecorrelation coefficient between arm and medial plantar BFI signals thathave been band-pass filtered.

One Dimensional Data Analysis of Power Spectrum

Two parameters for one-dimensional analysis of the Power Spectrum wereevaluated, based on the Low Frequency Oscillation Index (“LFI”)characteristics within the frequency band between 0.045 Hz and 0.10 Hz,and are described above. LFI_(M) is defined as the maximum amplitude inthe 0.045-0.10 Hz frequency band, and assumes that most of the lowfrequency oscillation (LFO) signals are explained by one single peakwithin the LFO frequency range of 0.045 to 0.10 Hz. In another words, itassumes that the frequency of the LFO signal does not vary appreciablyduring 5 minutes of data acquisition time. In contrast, LFI_(A) isdefined as the area under the curve within the 0.045-0.10 Hz frequencyband, is a more suitable metric if one assumes that the frequency of LFOchanges significantly within this frequency range during the acquisitiontime. In some embodiments, other frequency bands can be utilized for aparticular index depending on the desired clinical result. For example,the frequency in some embodiments could be less than 0.15 Hz, or lessthan about 0.10 Hz.

Results

Using the BFI data taken during this study, the LFI_(M) and LFI_(A)measurements calculated for each volunteer/patient are shown in FIGS. 9Aand 9B, respectively. FIGS. 9A and 9B illustrate boxplots of lowfrequency oscillation index (LFI) from 26 healthy and 26 ischemicpatients, assessed in two different methods: maximum-based (LFI_(M)) inFIG. 9A, and area-based (LFI_(A)) in FIG. 9B. Double sided t-testp-values for medial plantar (MP), calcaneal (C), and deltoid (Arm),respectively, are: 0.00027, 0.022, 0.20 for LFI_(M), and 0.0015, 0.016,and 0.41 for LFI_(A). Boxplots are drawn using MatLab, where thehorizontal line within the boxes indicates the median value, while theboxes indicate 25 to 75 percentile values, and crosses are outliers.

Receiver operating characteristic (ROC) curves were plotted to assessthe diagnostic accuracy of this test in distinguishing ischemic fromnormal populations. In a ROC curve the true positive rate (Sensitivity)is plotted as a function of the false positive rate (100-Specificity)for different cut-off points. Each point on the ROC curve represents asensitivity/specificity pair corresponding to a particular decisionthreshold. One metric used to determine the accuracy of a test is theArea Under the Curve (AUC) of an ROC plot: with an AUC of 0.9 to 1representing excellent discrimination, while an AUC of 0.5 representinga worthless test. A test with perfect discrimination (no overlap in thetwo distributions) has a ROC curve that passes through the upper leftcorner (100% sensitivity, 100% specificity) and an AUC of 1. Thereforethe closer the ROC curve is to the upper left corner, the higher theoverall accuracy of the test.

FIGS. 10A and 10B illustrate receiver operating characteristic (ROC)curves for LFI_(M) and LFI_(A) respectively measured in the medialplantar (MP) region. Area under the curve (AUC) is 0.7805 and 0.7322 forLFI_(M) and LFI_(A), respectively. Dashed curves are results ofnonlinear curve fitting.

As MP shows the smallest p-values for both LFI_(M) and LFI_(A) cases inFIGS. 9A-9B, MP data were used to draw ROC curves in FIGS. 10A-10B. AUCof the ROC curves of around 0.75 or higher showing a decentdiscriminating power. By way of comparison, Figoni et al (J. Rehab ResDev 2006: 43 (7) 891-904) report that TcPO₂ has an AUC of 0.82 indiscriminating between healthy subjects, and ischemic patients(identified as prospective candidates where unilateral transtibialamputation was imminent or scheduled because of lower-limb ischemia).The ischemic group of patients in the Figoni study however suffered froman extreme degree of ischemia in that the decision for an amputation ata level much above the site of TcPO₂ measurement had already been made.The patients in the study described above however were typical patientsin an out-patient setting, with none requiring amputations at the timeof testing. Despite this difference in the degree of ischemia betweensubjects in this study and the Figoni study, the AUC is similar betweenthe studies suggesting a much greater ability for LFI to distinguishsubtle differences in the degree of ischemia compared to TcPO₂.

The data in FIGS. 9A-10B indicate that LFI_(M) can be superior toLFI_(A) in some cases in its ability to distinguish ischemic foot tissuefrom healthy foot tissue, and that the distinction is particularlypronounced when the measurement is taken at the medial plantar area ofthe foot, where the p-value is statistically significant, and as smallas 0.00027, or even less.

The distinction between healthy and ischemic medial plantar tissue is,in some cases, statistically more highly significant when using LFI_(M)as the relevant index. Not to be limited by theory, a possibleexplanation for this may lie in the fact that LFI_(M) provides a moresnapshot insight relative to LFI_(A). In other words, LFI_(M) is ameasure of the maximal amplitude change, and expect healthy vessels withhigher elasticity and better rheology of blood flow would be expected tomanifest higher LFI_(M) values. In contrast, the LFI_(A) is an averagedmeasure of LFO, meaning that it averages out the multiple oscillatorychanges in a vessel into one averaged change represented by the areaunder the curve. Given that LFI_(A) is also capable of distinguishinghealthy versus ischemic tissue, it is possible that LFI_(A) does reflectoverall functions of elasticity and rheology over a period of time. Itmay simply be that, for a 5 minute reading such as that used in thisstudy, LFI_(M) is a more discriminatory index than LFI_(A). Thishypothesis is supported by smaller p-values associated with the use ofLFI_(M) versus LFI_(A). In some embodiments, longer or shorter readingperiods can be utilized, such as about 1, 2, 3, 4, 6, 7, 8, 9, 10, 15,20, 25, or 30 minutes as non-limiting examples.

This distinction, as shown in FIGS. 9A-9B, in some cases is most clearlyseen in the medial plantar, relative to the calcaneal area of the foot,and the deltoid. The medial plantar vasculature depends upon an intactpedal-plantar arch for blood supply and it is at this level thatocclusive arterial disease most commonly presents. The medial plantar istherefore much more vulnerable to ischemia, in contrast with thecalcaneal circulation which is dually supplied by the peroneal and theposterior tibial vessels. The deltoid region is much less affected thanthe feet, if at all, as significant upper limb arterial disease is rarein atherosclerosis and/or diabetes.

In some embodiments, an LFI_(A) value of less than about 130, 127.5,125, 122.5, 120, 117.5, 115, 112.5, 110, 107.5, 105, 102.5, 100, 97.5,95, 92.5, 90, 87.5, 85, 82.5, 80, 77.5, 75, 72.5, 70, 67.5, 65, 62.5,60, 57.5, 55, 52.5, 50, or less can serve as a pre-determineddiscriminatory cut-off value between a first population and a secondpopulation and indicate a risk factor for a characteristic or a diseasecharacteristic, e.g., ischemia, such as severe ischemia, and notify theclinician by prompting an audible, visual, or other signal, such asvisually on the display, for example.

In some embodiments, an LFI_(M) value of less than about 15, 14.5, 14,13.5, 13, 12.5, 12, 11.5, 11, 10.5, 10, 9.5, 9, 8.5, 8, 7.5, 7, 6.5, 6,or less can serve as a pre-determined discriminatory cut-off valuebetween a first population and a second population and indicate a riskfactor for a characteristic or a disease characteristic, e.g., ischemia,such as severe ischemia, and notify the clinician by prompting anaudible, visual, or other signal, such as visually on the display, forexample.

Multi-Dimensional Data Analysis of Power Spectrum

In addition to the parameters LFI_(M) and LFI_(A) described above, thereare other parameters or methods of analyzing the BFI data for thepurposes of discriminating between the two patient populations. In someembodiments, multiple independent parameters can be utilized inconjunction in order to more accurately discern to which population apatient belongs.

Analysis of multi-dimensional data sets can be facilitated by the use ofvarious strategies including, but not limited to, the use of artificialneural networks (ANN), extreme learning machines (ELM), and supportvector machines (SVM). In particular, an SVM is a means to define ahyperplane in multi-dimensional space that discriminates between twopopulations. In some embodiments, a SVM can be utilized to processmulti-dimensional inputs comprising parameters such as, but not limitedto, relative signal powers in specific frequency bands of a particularanatomical BFI signal, absolute signal powers in specific frequencybands of a particular anatomical BFI signal, and/or correlationcoefficients between band pass filtered BFI signals.

In some embodiments, an SVM can utilize one, two, or more of thefollowing five independent inputs (as described in Table 1) from eachpatient from the data set described above: Band pass (0.001 Hz to 0.110Hz) relative power of the calcaneal BFI; Band pass (0.001 Hz to 0.110Hz) relative power of the medial plantar BFI; Band pass (0.471 Hz to0.478 Hz) absolute power of the deltoid BFI; Band pass (0.471 Hz to0.478 Hz) absolute power of the medial plantar BFI; and/or Band pass(0.341 Hz to 0.351 Hz) correlation coefficient between deltoid andmedial plantar BFI.

When run against the same dataset of 26 healthy/26 ischemic patients,the SVM achieved, in one embodiment, an accuracy of 0.961, a sensitivityof 0.961, and a specificity of 0.961. The ROC of this SVM is shown inFIG. 11, which illustrates a ROC curve for a 5-dimensional SVM utilizingpatient BFI input parameters as noted in the preceding paragraph.

Statistical Analysis of a BFI Signal:

In some embodiments, the statistical parameters of the BFI signal canalso be used as a discriminator. The Flow Transform Level “FTL” is thestandard deviation of the BFI signal calculated at 2 Hz. FIG. 12 showshow this is derived from and relates to the time series BFI, e.g.,derivation of FTL from time series DSCA blood flow index (BFI) data,where intensity is measured at a frame rate of 60 Hz. Other frame rates,such as 30 Hz for example, can also be utilized depending on the timeduration selected.

The standard deviation of 5 minutes of Medial Plantar BFI data sampledat 1 Hz and 2 Hz was calculated, and the resulting ROC curves are shownin FIGS. 13A and 13B. FIG. 13A illustrates the ROC of Standard Deviationof BFI @ 1 Hz; FIG. 13B illustrates the ROC of Standard Deviation of BFI@ 2 Hz. As noted elsewhere herein, the amount of time data sampled canbe selected depending on the desired clinical result, such as about 30seconds, 45 seconds, 1 minute, 75 seconds, 90 seconds, 105 seconds, 2minutes, 3 minutes, 4 minutes, 5 minutes, or another time interval.Other frequencies other than 1 Hz or 2 Hz can be utilized as well, suchas a frequency of between about 0.5 Hz an about 10 Hz, or between about1 Hz and about 10 Hz.

If the standard deviation of the BFI at 2 Hz is focused on, and the dataset shortened and analyzed, a slow degradation of the AUC down to 2minutes can be observed, and a precipitous drop at 1 minute. This resultis shown in Table 2.

TABLE 2 Dependence of FTL AUC on sample time/data set size. Sample timeAUC for FTL 5 min 0.9645 4 min 0.9633 3 min 0.9554 2 min 0.9241 1 min0.7428

The Standard Deviation of BFI from calcaneal and arm also showssignificant difference between healthy and ischemic patients, but notstrongly as with the medial plantar. The p-values of three positions arecompared in FIGS. 14A-14C, which are box plots of FTLs in the medialplantar, calcaneal, and arm regions, respectively.

Assessment of Results

An AUC of the ROC curves of around 0.75 or higher showing a decentdiscriminating power, and an AUC exceeding 0.90 is considered excellentin some embodiments. By way of comparison, Figoni et al (J. Rehab ResDev 2006: 43 (7) 891-904) report that tcPO2 has an AUC of 0.82 indiscriminating between healthy subjects, and ischemic patients(identified as prospective candidates where unilateral transtibialamputation was imminent or scheduled because of lower-limb ischemia).The ischemic group of patients in the Figoni study however suffered froman extreme degree of ischemia in that the decision for an amputation ata level much above the site of TcPO2 measurement had already been made.In some embodiments, patients analyzed are typical patients in anout-patient setting, with none requiring amputations at the time oftesting. FIG. 14D illustrates a graph showing FTL values obtained in onestudy for healthy and ischemic patients on the Y axis and the patientnumerical identifier on the X axis.

Despite this difference in the degree of ischemia between subjects withrespect to the Figoni study, one-dimensional AUC using LFI_(M) can besimilar to the Figoni study suggesting a much greater ability for LFI todistinguish subtle differences in the degree of ischemia compared toTcPO2. When utilizing multiple parameters in our SVM, an AUC of 0.969 orbetter can be achieved, far exceeding the performance reported fortcPO2.

Using FTL (Standard Deviation of BFI @ 2 Hz) an AUC of 0.9645 with asingle parameter can be achieved from a single sensor located at themedial plantar. This greatly simplifies the measurement in some casesand can increase the utility and ease of implementation of technique forclinical diagnostic and/or screening applications.

In some embodiments, an FTL value of less than about 10, 9.75, 9.5,9.25, 9, 8.75, 8.5, 8.25, 8, 7.75, 7.5, 7.25, 7, 6.75, 6.5, 6.25, 6,5.75, 5.5, 5.25, 5, 4.75, 4.5, 4.25, 4, 3.75, 3.5, 3.25, 3, 2.75, 2.5,2.25, 2, or less can serve as a pre-determined discriminatory cut-offvalue between a first population and a second population and indicate arisk factor for a characteristic or a disease characteristic, e.g.,ischemia, such as severe ischemia, and notify the clinician by promptingan audible, visual, or other signal, such as visually on the display,for example.

Referring back to FIGS. 1D-1H, DOF sensors can be separately placed atdifferent topographical regions of the foot, for example the DOF sensorscan be placed at each of the pedal angiosomes using separate supportstructures. In another embodiment, however, a plurality of DOF sensorscan be incorporated into a single support structure for simultaneousmeasurement of different pedal regions, for example the pedalangiosomes. One such embodiment is illustrated in FIGS. 15A-15C. Aside-firing DOF sensor is shown in FIG. 15A. As illustrated, light froma source can enter the sensor 602 through input cable 604, and can exitthe sensor 602 through the output cable 606 towards the detector. Insome embodiments, the input cable and the output cable can be bundledtogether. Rather than having the cable oriented perpendicular to thesurface of the tissue to be measured, in this side-firing sensor thecable is oriented substantially parallel, with an internal prism,mirror, or other optical element redirecting light downwards towards thetissue. As a result, the DOF sensor 602 can be laid flat against thesurface of the area to be measured, with the cables 604 and 606extending substantially parallel to the surface. The overall effect is amore low-profile DOF sensor, with improved comfort, flexibility, andform-factor.

As used herein, the term “sensor” refers to the terminal end of the DOFsystem that makes contact with the sample, for example the patient'sskin. The sensor may include an input optical fiber coupled to a sourceand an output optical fiber coupled to a detector. In other embodiments,the sensor may comprise receptacles configured to removably receive suchoptical fibers. The sensor defines the point at which input light isinjected into the sample surface and the point at which scattered lightis detected from the sample surface. In the illustrated embodiment, theDOF sensor 602 is substantially flat. However, in various embodiments,other shapes are possible. For example, the DOF sensor may be providedwith a curved surface, for example contoured to correspond to contoursof a patient's body. A DOF sensor may include a concave surface tocorrespond to the curvature of a wearer's plantar arch, for example. Insome embodiments, the DOF sensor can be malleable to permit curvatureand flexure to correspond to a patient's body. As noted above, thedistance of separation between the source and the detector affects thepenetration depth of measured light. More specifically, the significantdistance is that between the position on the surface of the tissue atwhich light is injected, and position on the surface of the tissue atwhich light is detected. Accordingly, the side-firing DOF sensor 602 maybe modified to provide for different penetration depths depending on thepart of the body in which blood flow is to be measured. If the DOFsensor is adapted for use in measuring relatively deep blood flow, thesource-detector separation can be greater than for a DOF sensor adaptedfor use in measuring relatively shallow blood flow. In some embodiments,this distance can be variable within an individual DOF sensor. Forexample, a mechanism may be provided allowing for the source input fiberand/or the detector output fiber to be moved along the length of the DOFsensor to modify the distance therebetween. For example, in someembodiments the source input fiber may be substantially fixed inrelation to the sensor, while the detector output fiber is movable.Conversely, in some embodiments the detector output fiber can besubstantially fixed in relation to the sensor, while the source inputfiber can be movable. In some embodiments, the movable fiber can beslidable along the sensor, with a latch, screw, detent, or otherstructure provided to releasably fix the location of the movable fiberafter a pre-selected distance has been set. In some embodiments, themovable fiber can be mounted onto a support that is threadably mated toa screw, such that rotation of the screw causes the support, and therebythe movable fiber, to be advanced closer to or further from the fixedfiber. Various other configurations are possible. In other embodiments,various optical components within the interior of the DOF sensor can beprovided to alter the effective source-detector distance. For example,the positions of the fibers may be fixed, while internal prisms ormirrors or other optical components can be adjusted to direct the light(incident light from the source or scattered light to the detector) toor from different locations.

FIG. 15B illustrates, as one example of a support structure, a coversock 608 designed to slip over the patient's foot. As shown in FIG. 15C,a plurality of side-firing DOF sensors 602 can be carried by a coversock. In some embodiments, the side-firing DOF sensors 602 are arrangedat positions corresponding to different pedal angiosomes. Since each DOFsensor 602 can be made thin and flexible, they can be sewn or otherwiseattached to the cover sock 608 at the appropriate positions. The opticalfibers can be bundled and guided outside the foot covering 608 andconnected to an analyzer. With this design, applying the multiple DOFsensors to a patient's foot can be quick and essentially foolproof,which is particularly advantageous in the hectic environment of anoperating room or catheterization lab.

FIGS. 15D and 15E illustrates another example of a support structure andDOF sensor. As illustrated, the DOF sensor 610 includes bundled wires612 extending therefrom. The bundled wires 612 include both the inputand output optical fibers, as described above. A retention ring 614 isconfigured to surround the bottom-facing edge of the DOF sensor 610. Theretention ring 614 can be affixed to a surface (e.g., a patient's skin)via adhesive pads 616. The adhesive pads 616 can take a variety offorms, including, for example Tegaderm™ Film. In other embodiments,adhesive material is deposited onto the retention rings without the useof separate adhesive pads.

As illustrated, the retention ring 614 can define an aperture configuredto receive the DOF sensor 610 therein. In various embodiments, theretention ring 614 can include one or more retention elements configuredto releasably mate with corresponding retention elements on the DOFsensor 610. The engagement of corresponding retention elements therebyreleasably locks the sensor 610 into position with respect to theretention ring 614. In various embodiments, a latch, screw, detent, orother structure can be provided to releasably fix the DOF sensor 610 tothe retention ring 614.

Various other support structures are possible. For example, in someembodiments the DOF sensors may be carried by a series of strapsconfigured to be wrapped around a patient's foot so as to position theDOF sensors appropriately with respect to the desired measurementregions of the pedal topography, for example different pedal angiosomes.In some embodiments, the DOF sensors may be carried by a sheet offlexible material to be wrapped around the patient's foot. In someembodiments, the support structure may be configured to carry one, two,three, four, five, or more DOF sensors. In some embodiments, two or moresupport structures may be provided for a single patient. For example afirst support structure may carry two DOF sensors and be positioned overa first portion of a patient's foot, while a second support structuremay carry two additional DOF sensors and be positioned over a secondportion of the patient's foot. In various embodiments, the supportstructure may be wearable, for example it may be a garment such as acover sock, shoe, etc. In some embodiments, the support structure caninclude a strap or series of straps. In other embodiments, the supportstructure can comprise an adhesive material by which one or more DOFsensors can be attached to a patient's skin. For example, in someembodiments, each of the DOF sensors can be provided with an adhesive onthe tissue-facing side so as to ensure that the sensors contact theskin. In some embodiments, mechanical pressure can be applied to the DOFsensors to ensure that they are pressed against the skin—for example anexternal wrap may be used, or the elasticity of a cover sock or otherfoot covering may itself be sufficient to ensure that the DOF sensorsare adequately held against the skin. In some embodiments, DOF sensorscan be embedded into a foot plate sensor such as those used bypodiatrists. An individual may step onto the foot plate, and one or moreDOF sensors carried by the foot plate can measure absolute and/orrelative blood flow at various locations on the foot.

In some embodiments, each DOF sensor may be carried by a differentsupport structure. In other embodiments, a support structure can beconfigured to carry any number of DOF sensors, for example two, three,four, five, or more. In various embodiments, the support structure canbe configured such that, when the support structure is positioned over apatient's foot, the position of the DOF sensors correspond to differenttopographical locations in the foot including selected pedal angiosomes.The support structure can be configured to carry DOF sensorscorresponding to any combination of topographical locations in the footincluding pedal angiosomes. For example, in one embodiment a supportstructure may be configured to carry DOF sensors adapted to measureblood flow at the calcaneal branch of the posterior tibial artery and atthe calcaneal branch of the peroneal artery. In another embodiment asupport structure can be configured to carry DOF sensors adapted tomeasure blood flow at the medial plantar artery, the lateral plantarartery, and the calcaneal branch of the posterior tibial artery. Variousother configurations are possible, such that the support structure canbe tailored to provide DOF sensors at the desired measurement locations.

FIG. 16 is a flow diagram of a method for analyzing relative blood flow.The process 700 begins in block 702 with positioning at least one DOFsensor on a patient's foot at a location corresponding to a pedalangiosome. As noted above, in some embodiments a plurality of such DOFsensors may be positioned at various places on a patient's foot, orother places on the patient's body. In some embodiments, a plurality ofsuch DOF sensors can be used to obtain simultaneous measurements fromdifferent topographical locations in the foot including differentangiosomes. The process 700 continues in block 704 with obtainingmeasurement of absolute and/or relative blood flow using the DOF sensor.As noted above, DOF techniques can provide an autocorrelation functionindicative of the absolute and/or relative blood flow within the tissue.The process 700 continues in block 706 with signaling the absoluteand/or relative blood flow to the operator. For example the signal maybe provided via visual, audible, or tactile communication. In someembodiments, the absolute and/or relative blood flow can be signaled tothe operator in substantially real-time, for example within 1 second ofmeasurement. In some embodiments, a display may be provided that showsthe autocorrelation functions, a chart of blood flow indices (BFIs), orother indicator of the absolute and/or relative blood flow. Such adisplay can provide the operator with real-time feedback to guideintra-operative decision-making.

As described above, sensor head designs for DOF sensors traditionallyemploy fibers with either metal or ceramic ferrules to protect the fibertip, hence the typical sensor head design is limited to a verticalcontact scheme where light out of the fiber is directly coupled into asample. The vertical fiber design suffers from a number of disadvantageswhen used in applications for blood perfusion monitoring: it adds bulk,height and positional instability to the sensor head; it may requireadditional means of support to achieve stable and consistent contactwith the skin; and for these reasons, it may cause patient discomfortafter prolonged application.

Therefore, it is advantageous to implement a low profile generallyhorizontal contact sensor head that is both simple and cost-effective.FIGS. 17A-17C illustrate an embodiment of such a DOF sensor head. FIG.17A illustrates a schematic cross-section of the sensor head 800, andFIGS. 17B and 17C illustrate plan views of two possible embodiments forthe sensor head 800. As illustrated, a support structure includes areceptacle member 804 with a groove to receive the optical fibers 806therein, and a reflector member 808 with a reflecting surface. Asillustrated, optical fibers 806 are applied generally horizontally ontothe surface of the sample 810, and part of the fiber body is disposedwithin a groove in a receptacle member 806, and the distal tip of thefiber 806 configured to be positioned between the surface of sample 810and a reflecting surface of the reflector member 808. Light coming outof the source fiber tip will be reflected off of the reflecting surfacein this gap and will be directed towards the sample 810. For thedetector fiber, the reverse will happen: only those light paths thatfall within the acceptance cone will be reflected off of the reflectingsurface and collected by the fiber. In some embodiments, the reflectingsurface may comprise a sheet of aluminum foil mounted onto a compliantbacking such as a rubber, silicone, or foam pad. It will be appreciatedthat a wide range of materials may be utilized as reflectors includingmetal foils, metal films, optically reflective coatings, interferencegratings, nanostructured meta-materials, or any other material withsuitable optical properties.

When applied to a sample, the planar DOF sensor places the fiber inoptical communication with the sample. In some embodiments an opticallytransparent sterile barrier comprising at least one opticallytransparent layer may be disposed between the fiber and the sample. Theat least one optically transparent layer may be configured to haveadhesive coatings to facilitate attachment of the planar DOF sensor ontothe surface of the sample/tissue. For example, surgical tape maycomprise a support configured to receive the DOF sensor thereon, and tocouple the DOF sensor to the sample.

FIGS. 18A-18D show one embodiment of the supports fabricated using 3Dprinting, with a support comprising an adhesive layer that is disposedbetween the patient/tissue and the optical fibers. FIGS. 18A and 18Billustrate the support member 902, with FIGS. 18C and 18D showing topand bottom views, respectively, of the sensor heads 900 prepared with alayer of surgical adhesive tape 912 to be disposed between the patient'sskin and the fibers. In FIGS. 18C and 18D, the reflector pads 908 andtips of fibers 906 are obscured by the adhesive liner of the surgicaltape 912. In other embodiments, the at least one optically transparentlayer may not have an adhesive coating, whereupon the planar DOF sensormay be attached to the sample by the application of surgical tape, amechanical clamp, adjustable strap, or other means.

FIG. 19 illustrates a plurality of DOF sensors 1000 attached to apatient's foot. With a source-detector separation of approximately 1.5cm on a healthy human foot, arterial cuff occlusion protocolobservations display typical blood perfusion variations—e.g., a suddendecrease and plateauing during occlusion, and sharp overshoot andsubsequent recovery to baseline value after release of the cuffpressure. FIG. 20 illustrates a DOF sensor attached to a patient's hand.The computer screen indicates a decrease in blood perfusion duringarterial cuff occlusion and subsequent reactive hyperemia, indicatinghealthy blood flow in the hand. In the illustrated graph, two sets ofcuff-occlusion are shown with two distinct peaks of reactive hyperemia.

Advantages of the planar DOF sensor head include its low weight, itsstability during prolonged application, and a higher level of patientcomfort. Its performance is not compromised compared to a verticalsensor head design, and it can be utilized in any optical transmissionmeasurement system in semi-infinite geometry.

Some embodiments may also include memory to store measured or computeddata (such as but not limited to BFI, FTI, raw DOF signals), and thecapacity to transmit/receive measured or computed data to/from at leastone website/database. The at least one website/database can providepatients and clinicians access to the measured or computed data,process/analyze the data and provide notifications to clinicians and/orpatients. These notifications may include, but are not limited to,alerts when patient should seek medical attention, updates to cliniciansthat new patient data is available for review, etc. The data can bestored in a manner and compliant with standards applicable to electronichealth records of hospitals and diabetic/podiatry/geriatric/communitycare centers. Such a system can enable clinicians, care givers, andfamily members to remotely monitor patients, and can be especiallyrelevant in resource limited regions where access and travel to clinicalcare centers are limited and/or difficult. By remotely assessingpatient's health, it will be possible to improve clinical care byensuring that only essential travel is undertaken.

In some embodiments, systems and components as described herein can takethe form of a computing system that is in communication with one or morecomputing systems and/or one or more data sources via one or morenetworks. The computing system may be used to implement one or more ofthe systems and methods described herein. While various embodimentsillustrating computing systems and components are described herein, itis recognized that the functionality provided for in the components andmodules (which may also be referred to herein as engines) of computingsystem may be combined into fewer components and modules or furtherseparated into additional components and modules. For example, acommunications engine may include a first module in communication with adiagnostic imaging modality and a second module in communication with adestination modality. Modules can include, by way of example,components, such as software components, object-oriented softwarecomponents, class components and task components, processes, functions,attributes, procedures, subroutines, segments of program code, drivers,firmware, microcode, circuitry, data, databases, data structures,tables, arrays, and variables. Any modules can be executed by one ormore CPUs.

A software module may be compiled and linked into an executable program,installed in a dynamic link library, or may be written in an interpretedprogramming language such as, for example, BASIC, Perl, or Python. Itwill be appreciated that software modules may be callable from othermodules or from themselves, and/or may be invoked in response todetected events or interrupts. Software instructions may be embedded infirmware, such as an EPROM. It will be further appreciated that hardwaremodules may be comprised of connected logic units, such as gates andflip-flops, and/or may be comprised of programmable units, such asprogrammable gate arrays or processors. The modules described herein canbe implemented as software modules, but may be also represented inhardware or firmware. Generally, the modules described herein refer tological modules that may be combined with other modules or divided intosub-modules despite their physical organization or storage. In addition,all the methods described herein may be executed as instructions on aCPU, and may result in the manipulation or transformation of data.

In some embodiments, hardware components of the system includes a CPU,which may include one, two, or more conventional microprocessors. Thesystem further includes a memory, such as random access memory (“RAM”)for temporary storage of information and a read only memory (“ROM”) forpermanent storage of information, and a mass storage device, such as ahard drive, flash drive, diskette, or optical media storage device.Typically, the modules of the system are connected using a standardbased bus system. In different embodiments, the standard based bussystem could be Peripheral Component Interconnect (“PCP”), Microchannel,Small Computer System Interface (“SCSI”), Industrial StandardArchitecture (“ISA”) and Extended ISA (“EISA”) architectures, forexample.

In accordance with some embodiments, systems may be operatively coupledto a destination modality, such as, for example, an electronic medicalrecord (“EMR”). EMRs may be any software or hardware-software systemconfigured to store and provide access to electronic medical data. Inaccordance with various embodiments, EMRs may be at least one of anelectronic medical record, an electronic health record, and the like. Insome embodiments, systems and components thereof can be operativelycoupled to a destination modality that can be an email or othermessaging modality; SAMBA, Windows, or other file sharing modality; FTPor SFTP server modality; a VPN; a printer; and the like.

In accordance with some embodiments a system may comprise one, two, ormore software modules, a logic engine, numerous databases and computernetworks configured to provide a user with access to various modalitiesas described herein and/or an EMR. Systems may be configured such thatpatient data, or no patient data is recorded by the system. While thesystem may contemplate upgrades or reconfigurations of existingprocessing systems, changes to existing databases and businessinformation system tools are not necessarily required. Systems may beimplemented or integrated into existing healthcare informationmanagement systems, such as EMRs, without changes to the EMR system, andmay interface with other modalities without changes to the communicationsystem of the modality.

In accordance with some embodiments, systems may be software orhardware-software systems. For example, systems can include acommunication engine configured to receive and transmit medicalinformation operatively coupled to an information converter configuredto render diagnostic medical information in a suitable format forstorage in a patient EMR; a work list engine configured to create a userselectable task list from orders captured at an EMR and selectable by auser at a medical diagnostic modality; and an event log configured witha user selectable record of transactions and/or errors in datatransmission and/or data conversion performed by the system.

In accordance with some embodiments, communication engine may be anysoftware or hardware software-system configured to receive and/ortransmit data. Communication engine may be configured to transmit andreceive data over a variety of network interfaces including wired andwireless networks or a combination thereof, such as via Ethernet,802.11x, Bluetooth, FireWire, GSM, CDMA, LTE, and the like.Communication engine may also be configured to transmit and/or receivedata with file transfer protocols such as TCP/IP, as well as variousencryption protocols, such as, for example, WEP, WPA, WPA2, and/or thelike.

Furthermore, in some embodiments, a communication engine may beconfigured as an active or passive module. When communication engine ispassive, it may be configured to be discoverable by various elements ofa larger healthcare management system. In this way, communication enginemay be configured to receive a command or request from a medicaldiagnostic modality for a user selected patient, such that thecommunication engine may transmit the request to an EMR, receive thepatient data for a specific patient from the EMR, and transfer thepatient data from the EMR to the medical diagnostic modality. As such,communication engine is only configured to receive and transmit data. Insome embodiments, communication engine is not configured to collect,capture, or mine data from, either, an EMR or a medical diagnosticmodality.

Clinical Applications

Embodiments of Derivative Indices of DSCA provide a direct assessment ofmicrovascular vasomotion in the patient. Endothelial dysfunction causedby diabetes (Kolluru et al in Intl J of Vascular Med 2012) underminesnormal vasomotion, leading to delayed vascular re-modeling and woundhealing. The Derivative Indices therefore can in some embodimentsprovide means to better assess the healing capacity of patients (bothdiabetic and non-diabetic) and hence direct the optimal use of woundcare therapy. Additional use for the Derivative Indices could be forscreening patients for peripheral vascular disease, determining theefficacy of a revascularization procedure, such as a bypass, stent,graft, angioplasty, or other procedure, either intraoperatively orpostoperatively; predicting response to advanced wound therapies such asHBOT, and determining the optimal sites for limb amputation, forexample. Other applications of this technique include, for example, theassessment of plastic surgery grafts or flaps for tissue viability. Insome embodiments, DOF sensors can be used to assess blood flow in thefoot, ankle, calf, thigh, hand, arm, neck, or other anatomicallocations. In some embodiments, the DOF sensors can be positioned withinthe body, for example within natural orifices, such as the esophagus,stomach, small intestine, colon, or uterus for example to assess bloodflow. In various such embodiments, DOF sensors can be disposed inaccordance with angiosome theory.

Ischemic Foot Screening

One, two, or more of the Derivative Indices may be used as a tool toscreen for ischemic feet, particularly for diabetic patients where thepresence of neuropathy as part of the diabetic disease progression meansthat claudication is often not a reliable manifestation of the severityof underlying peripheral arterial disease, e.g., the patient feels nopain due to diabetic neuropathy, rather than because there is noatherosclerotic disease.

As a screening tool should ideally be small, compact, inexpensive, andwidely deployable and utilized by staff with minimal training, in someembodiments the system for screening ischemic feet may be implementedusing a small, battery powered, portable, blood perfusion monitorconsole comprising a single sensor that is attached to the patient'sfoot for measurement duration of, for example, 10 seconds to 10 minutes.The recorded time series blood perfusion can then be processed into apower spectrum via an internal processor. Alternatively, the time seriesdata may be telemetered to a distributed computational network forprocessing. Results of the calculated one or more Derivative Indices canthen be reported directly to the physician's office or care giver forfurther follow-up. Alternatively, caregivers or clinicians may remotelyaccess results via the internet, smart phone, or othertelecommunications device. Patients who present with endothelialdysfunction and/or ischemia can then be referred to primary care centersfor more directed evaluation and therapy.

Diabetic feet are also at risk of ulceration from a combination ofischemia, high plantar pressures from bio-mechanical change in the footas well as neuropathy. In clinical practice, the combination of thesethree factors leads to a diagnosis of a diabetic foot at risk ofulceration (“DFAR”). Annually, 25% of diabetics are thus diagnosed to beat risk of ulceration, and 50% of such diagnosed patients subsequentlyundergo a major or minor amputation of foot tissue.

Some approaches measure the three diagnostic indicators separately—theankle-brachial index (“ABI”) can be used to measure ischemia, while apressure footplate can be used to measure plantar pressure, and apressure-sensitive monofilament that buckles at a pre-determinedpressure but is not felt on application by the patient can be used todiagnose neuropathy. There are multiple disadvantages of theseapproaches, including (a) ABI measurements are highly variable dependingon the procedural protocol that in turn varies from hospital tohospital. The position of the patient is highly material as anklesystolic pressure is affected by posture—1 mmHg higher for each inch theankle is below the heart; (b) the presence of calcified vessels indiabetic feet can generate falsely high readings of ABI; and (c) theclinic workflow can become congested at the physician's desk as it takesa medically qualified doctor to subjectively interpret on a case-by-casebasis three different reports for ischemia, plantar pressure, andneuropathy in order to make a determination of a diabetic foot at risk.It typically takes 30 minutes or more for a physician to run these testsand make a diagnostic determination.

Some embodiments described herein include one, two, or more flowsensors, such as diffuse optical flow (DOF) sensors configured tomeasure one, two, or more parameters relevant to blood flow, andoperably connectable to one, two, or more anatomical regions ofinterest, such as a foot or hand for example. The sensors are inoperative wired or wireless communication with a hardware console unitconfigured to receive the parameters from the sensors and performpredetermined calculations as described elsewhere herein. Someembodiments described herein comprise a pressure-sensitive footplateinto which is embedded at least one diffuse optical flow (DOF) sensorheads which will be in optical communication with an angiosome or othertopographic location of the patient's foot so as to take a measurementbased on one or more of the Derivative Indices, and, optionally, atleast one DOF reference sensor head that can be applied to a suitablelocation on the patient such as the thumb or the earlobe, to obtain areference reading for computation of the FTI. The device may generate aquantitative readout per foot of the absolute BFI and/or FTI and/or anyother Derivative Index, as well as the plantar pressure, each withobjective threshold criteria for indicating whether a foot needs furtherphysician review and therapeutic or pre-emptive intervention. The devicerepresents a simple, objective and intuitive method of diagnosing adiabetic foot at risk of ulcer in a way that removes inter-operatorvariation and avoids multiple tests. In some embodiments, to generate areport of the relevant data, the patient merely has to stand on thefootplate device for a short period of time, for example approximately30 seconds with an adhesive sensor head affixed to one thumb or otherreference point. Such a simple outpatient tool can be easily used bynurses, clinical technicians, physiotherapists etc. in the diabetes orpodiatry care community to more efficiently triage diabetic feet at riskand thereby ease the workflow congestion caused by the chronic shortageof physicians in many aging communities worldwide.

Guiding Wound Management

Current techniques utilized to assess wound healing potential aresub-optimal. TcPO₂ measurements have been shown to be poor predictors ofHBOT outcome (Fife et al, Wound Rep Reg 2002; 10: 198-207). Skinperfusion pressures are in fact better predictors of wound healing thanTcPO₂ (Lo et al in Wounds 2009), though with a diagnostic accuracy ofless than 80% for an SPP cutoff value of <30 mmHg (Castruonuovo et al inJVS 1997).

It is possible that TcPO₂ and SPP will never reach the highest levels ofdiagnostic accuracy demanded by the clinical community, as both arelimited by the fact that measurements are only skin deep. Studies byRucker et al (Rucker et al in Am J Physiol Heart Circ, 2000) showed thatunder critical perfusion conditions, it is the vasomotion and flowmotion in the skeletal muscle that preserve nutritive function tosurrounding tissue like skin, subcutis and periosteum, which areincapable of this protective mechanism. In addition, the impairedendothelial dysfunction as seen in diabetes directly impairs vasomotorfunction (Kolluru et al in Intl J of Vascular Med 2012) leading todelayed vascular re-modeling and wound healing. It follows thereforethat measurement of either just partial pressure of oxygen (TcPO₂) orperfusion pressure in the skin alone (SPP) does not reflect the criticalnature of the ischemia in the underlying tissue, and hence provides atbest a partial indicator/predictor of wound healing.

In contrast, the Derivative Indices directly measure the vasomotorfunction in tissue at a depth much greater than skin (up to 2 cm), andthus have the potential to be a superior predictor of wound healing, anda powerful tool to guide the appropriate therapy for wound healing. Insome embodiments, blood flow can be measured at a depth of greater thanabout 2 mm, 4 mm, 6 mm, 8 mm, 10 mm, 12 mm, 14 mm, 16 mm, 18 mm, 20 mm,or more.

Conservative therapy for wounds (e.g. bandages, moist dressings) cansuffice to facilitate wound healing if the blood perfusion around thewound tissue is not compromised beyond the minimal threshold for passivehealing to occur. In cases where the perfusion is thus compromised,however, the inappropriate use of conservative wound therapy causes atime lag between the first presentment of a wound in a clinical settingto an effective therapy commensurate with the seriousness of the woundcondition. The TIME (Tissue viability, Infection control, Moisture,Epithelialization) model of wound care emphasizes the need for earlydiagnosis of tissue viability or otherwise in a wound, which diagnosiswill then drive the therapy pathway towards wound healing. The singlemost important determinant of tissue viability in a wound is its bloodsupply. The ability to assess the blood perfusion around the wound bedallows clinical decisions to be made regarding either (a) continuationof conservative therapy if tissue is viable or, (b) if blood perfusionis too severely compromised for successful conservative therapy, toprogress to more advanced wound care products like chemical debridingagents, or advanced wound therapies such as topical negative pressure,hyperbaric oxygen therapy etc. In more serious cases, the patient can bedirected to revascularization by peripheral interventional procedures.

Guiding Amputation Levels

The Derivative Indices may also have a role in predicting the success ofamputation healing. Amputation is typically performed on patients withsevere limb ischemia who cannot be treated with reconstructive vascularsurgery, patients with diabetic foot ulcers or venous ulcerations.Approximately, 85-90% of lower limb amputations in the developed worldare caused by peripheral vascular disease and poor wound healingaccounts for 70% of the complication cases that arises from amputation.In spite of the use of state of the art technologies to assessamputation level, the healing rate of below-knee amputation rangesbetween 30 and 92%, with a re-amputation rate of up to 30%.Post-amputation wounds fail to heal if the blood perfusion at theamputation level is inadequate to support wound healing. When thisoccurs, the surgical wound breaks down, often with superadded infection,and can add to revision amputation where the leg is amputated at ahigher level, or to the morbidity of the patient as well delays inpatient rehabilitation and prosthetic fitting. The ability to measureblood perfusion using one or more of the Derivative Indices may enablethe physician to better predict successful amputation healing atdifferent levels of the leg to be amputated. This will guide thephysician via objective criteria as to the appropriate level ofamputation to minimize patient pain and suffering while maximizing limbpreservation.

Screening for Hyperbaric Oxygen Therapy

Hyperbaric oxygen therapy to aid the healing of chronic non-healingwounds is currently directed by the measurement of TcPO₂ in the skinsurrounding the wound bed before and after the administration of 100%oxygen. HBOT involves the administering of oxygen at levels 2-2.5 timessea level in a chamber. The administration of HBOT as a therapy over along period of time is not only expensive and comes with manyundesirable side effects such as ear and sinus barotrauma, paranasalsinuses and oxygen toxicity of the central nervous system. (Aviat SpaceEnviron Med. 2000; 71(2):119-24.) Moreover, a retrospective study of1144 patients (Wound Rep Reg 2002; 10:198-207) indicated that 24.4% ofchronic wound patients who received HBOT obtained no benefit from it.There is therefore a need to better predict the success of HBOT for anygiven individual. Since measurements of the Derivative Indices are takenat tissue depths well below skin level, it holds potential for theability to identify those patients for whom HBOT may well be unsuitable.

Assessment of Surgical Flaps

A further use of the Derivative Indices in clinical practice lies insurgical procedures, particular in plastic and reconstructive surgery,where pedicled or free tissue flaps are used to cover wound defects.Skin, myocutaneous, fascio-myocutaneous and osseomyocutaneous flaps areused to reconstruct tissue defects that may result from trauma, surgeryfor tumors, infections or congenital diseases. These flaps depend uponthe blood supply from either their own blood vessels or frommicro-vascular reconstructions with the blood vessels in the vicinity ofthe recipient tissue bed for their survival. Both types of flaps(pedicled and free) are crucially dependent on the blood perfusionwithin them for the flaps to survive. Flap perfusion needs closemonitoring especially in the first few hours to days after thereconstruction procedure and early detection of loss of perfusion willhelp to direct the patient for further surgical procedures as needed toensure continued flap viability. Monitoring the perfusion of these flapseither via surface sensors or sensors within the flap tissue may guidethe physician towards an early intervention that can preserve theviability of the flap. The Derivative Indices can be potentially used tomonitor flap blood perfusion continuously in the post-operative periodand prevent flap loss due to delayed detection of flap ischemia.

Intravascular and/or Intra-Luminal Tissue Probes for Use in GuidingDecisions for Various Therapies

In another embodiment, a DOF sensor for blood flow assessment, e.g.,intravascular use comprises at least two fibers configured toemit/receive optical signals at their distal ends, that is delivered viapercutaneous and/or transluminal means into an organ or tissue bed thatallows for DCS or DSCA measurements of blood perfusion in tissue volumeswhich are in optical communication with the at least two fibers. Such anintravascular sensor may be configured to have a small cross-sectionsimilar to a guidewire of between about 0.01 to about 0.04 inches (orabout 250 microns to about 1 mm). The intravascular sensor may bedisposed within a flexible sheath that will protect it during delivery,and facilitate insertion of the probe into the target tissue, whereuponthe sheath may be partially retracted or the distal tip of the probepartially extended beyond the end of the sheath, so as to put the distalends of the at least two fibers in optical communication with the tissuewhose perfusion is to be measured.

Intravascular and/or intra-luminal tissue probes can enable thereal-time measurement of blood perfusion in visceral organs or tissue toguide decisions in various medical therapies, including currenttreatment protocols for cancer therapy and vascular malformations. Theseexamples are described in greater detail below. In some embodiments,systems and methods as disclosed herein can be utilized for thediagnosis and assessment of the efficacy of various therapeuticinterventions for a wide variety of indications, including transientischemic attacks and acute ischemic strokes (and the efficacy of aneurointerventional revascularization procedure, such as angioplasty orstent placement), ischemic bowel, pulmonary embolism, myocardialinfarction, and others. In some embodiments, systems and methods canalso measure active bleeding (such as GI bleeding) and confirming thecessation thereof. Other indications are described below.

(a) Measuring Tumor Vascularity and its Impact on Photodynamic Therapyas Well as Tumor Sensitization Measurements Before RadiofrequencyAblation

The following articles refer to the need for assessing tumor blood flowin directing radiotherapy, chemotherapy and photodynamic therapy, andare hereby incorporated by reference in their entireties. (Int. J.Radiation Oncology Biol. Phys 2003 V 55, No 4, pp 1066-1073, “Nitricoxide-mediated increase in tumor blood flow and oxygenation of tumorsimplanted in muscles stimulated by electric pulses”, B. F. Jordan,Bernard Gallez et al; The Oncologist 2008, 13:631-644 “Use of H₂ ¹⁵O-PETand DCE-MRI to Measure tumor blood flow”, Adrianus J de Langen et al;Radiat Res 2003 October 160 (4) 452-9 “Blood flow dynamics afterphotodynamic therapy with verteporfin in the RIF-1 tumor” Chen B Poque,et al) In brief, the potential for success for chemotherapy is higher inwell-perfused tumors. Prior knowledge of this can be used to identifythose patients likely to respond well to treatment and stream suchpatients with greater confidence for chemotherapy treatment.Quantitative measurement of tumor blood flow may also help calculatedoses of chemotherapeutic agents to be delivered, especially when suchchemotherapy is directly delivered into the tumor via intra-luminal orendovascular means. This will help to avoid the unnecessary and painfulchemotherapy of patients who are unlikely to benefit from treatment dueto the poor vascularity of their tumors.

Perfusion has also been shown to play a key role in the success ofhyperthermic treatments like radiotherapy and photodynamic therapy.Oxygen deficiency in tumors has been shown to reduce repose tonon-surgical treatment modalities like radiotherapy and chemotherapy.This oxygen deficiency may be caused by decreased tumor perfusion(diffusion-related hypoxia) or changes in red cell flux (acute hypoxia).Increasing tumor perfusion by various methods such as use of vasoactiveagents, carbogen breathing and electrical stimulation of skeletal musclesurrounding the tumor to increase tumor blood flow have been shownexperimentally to have radiosensitizing effects. Photo-dynamic therapy(PDT) uses the principle of light at specific wavelengths causing damageto tumor vasculature and rendering the tumor ischemic, i.e. starving thetumor of its blood supply. Success of PDT is thus assessed by the extentto which this ischemia is achieved. The ability to measure tumor bloodflow either by endovascular or intra-luminal means can thus help directthe use of these methods to enhance tumor response or to assess tumorresponse to these non-surgical therapies.

(b) Intravascular and/or Intra-Tissue Probes to Guide Injection ofSclerosing and Embolic Agents During Treatment of Vascular Malformations

Vascular malformations (“VMs”), such as arterio-venous malformations,are a network of abnormal small vessels that are formed spontaneously oroccur congenitally or following trauma to create an alternate conduit ofblood flow between arteries, veins and capillaries, bypassing the normalblood flow that originates from the artery through the capillary bed ofan organ or tissue and thence into the vein. Clinical indications fortreatment of a VM include local symptoms of pain, bleeding or ulcerationat the site of the VM, and significant cardiac strain (including highoutput cardiac failure) from the high volumes of blood that flow withinthese lesions. Superficial VMs may need treatment for cosmetic reasonsas well.

The treatment for VMs comprises injection via an endovascularmicro-catheter of a sclerosing agent such as absolute alcohol or sodiumtetradecylsulphate, which are toxic to blood vessels and cause sclerosisor scarring that closes up the small vessels within the VM. This may bethe sole procedure or as part of a surgical procedure wherein the volumeof blood flowing within the VM is reduced prior to surgical excision.Caution is required during this procedure because excessive injection ofthe sclerosing agent can lead to overflow into normal blood vessels,resulting in significant damage such as skin necrosis, limb loss, acutepulmonary hypertension, or even death. The challenge for the physicianis that a balance must be struck between injecting enough sclerosingagent to completely close up the VM, but not so much that the sclerosingagent leaks out and causes serious damage elsewhere. Real-time perfusionmonitoring of the VM can signal when blood flow has ceased within the VMor reduced sufficiently to allow surgical resection without significantloss of blood. This may instruct the physician that enough sclerosingagent has been injected and to avoid further injection, thereby reducingthe risk of an adverse outcome.

Various other modifications, adaptations, and alternative designs are ofcourse possible in light of the above teachings. Therefore, it should beunderstood at this time that within the scope of the appended claims theinvention may be practiced otherwise than as specifically describedherein. It is contemplated that various combinations or subcombinationsof the specific features and aspects of the embodiments disclosed abovemay be made and still fall within one or more of the inventions.Further, the disclosure herein of any particular feature, aspect,method, property, characteristic, quality, attribute, element, or thelike in connection with an embodiment can be used in all otherembodiments set forth herein. Accordingly, it should be understood thatvarious features and aspects of the disclosed embodiments can becombined with or substituted for one another in order to form varyingmodes of the disclosed inventions. Thus, it is intended that the scopeof the present inventions herein disclosed should not be limited by theparticular disclosed embodiments described above. Moreover, while theinvention is susceptible to various modifications, and alternativeforms, specific examples thereof have been shown in the drawings and areherein described in detail. It should be understood, however, that theinvention is not to be limited to the particular forms or methodsdisclosed, but to the contrary, the invention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the various embodiments described and the appended claims.Any methods disclosed herein need not be performed in the order recited.The methods disclosed herein include certain actions taken by apractitioner; however, they can also include any third-party instructionof those actions, either expressly or by implication. For example,actions such as “discriminating between two populations” includes“instructing the discriminating between two populations.” The rangesdisclosed herein also encompass any and all overlap, sub-ranges, andcombinations thereof. Language such as “up to,” “at least,” “greaterthan,” “less than,” “between,” and the like includes the number recited.Numbers preceded by a term such as “approximately”, “about”, and“substantially” as used herein include the recited numbers (e.g., about10%=10%), and also represent an amount close to the stated amount thatstill performs a desired function or achieves a desired result. Forexample, the terms “approximately”, “about”, and “substantially” mayrefer to an amount that is within less than 10% of, within less than 5%of, within less than 1% of, within less than 0.1% of, and within lessthan 0.01% of the stated amount.

What is claimed is:
 1. A computer-implemented method for discriminatingbetween at least a first population and a second population, the methodcomprising: measuring blood perfusion as a function of time to obtaintime series data; mathematically transforming the time series data intoa power spectrum; calculating at least one parameter of the powerspectrum within a specific frequency range; and using the at least onecalculated parameter as a discriminator for the first population and thesecond population.
 2. The method of claim 1, wherein at least the firstpopulation and the second population comprise two patient populations.3. The method of claim 1, wherein the first population comprises ahealthy control group and the second population comprises an ischemicpopulation.
 4. The method of claim 1, wherein measuring blood perfusionas a function of time comprises using an optical measurement method. 5.The method of claim 4, wherein the optical method is diffuse correlationspectroscopy.
 6. The method of claim 4, wherein the optical method isdiffuse speckle contrast analysis.
 7. The method of claim 4, wherein theoptical method is diffuse optical tomography.
 8. The method of claim 4,wherein the optical method is near-infrared spectroscopy.
 9. The methodof claim 4, wherein the optical method is laser Doppler flowmetry. 10.The method of claim 1, wherein measuring blood perfusion as a functionof time comprises using a non-optical measurement method.
 11. The methodof claim 10, wherein the non-optical measurement method is selected fromthe group consisting of an electrical measurement method and a magneticmeasurement method.
 12. The method of claim 1, wherein measuring bloodperfusion as a function of time comprises using an electrical ormagnetic measurement method.
 13. The method of claim 1, whereinmathematically transforming the time series data into a power spectrumcomprises using a Fourier transform.
 14. The method of claim 1, whereinmathematically transforming the time series data into a power spectrumcomprises using a fast Fourier Transform.
 15. The method of claim 1,wherein mathematically transforming the time series data into a powerspectrum comprises using a wavelet transform.
 16. The method of claim 1,wherein the specific frequency range is between about 0.001 Hz and about1000 Hz.
 17. The method of claim 1, wherein the specific frequency rangeis between about 0.001 Hz and about 0.1 Hz.
 18. The method of claim 1,wherein the specific frequency range is between about 0.045 Hz and about0.1 Hz.
 19. The method of claim 1, wherein the frequency range ofinterest is between 0.001 Hz and 0.045 Hz.
 20. The method of claim 1,wherein the at least one parameter is the area under the curve of thepower spectrum within the specific frequency range.